Chapter 10

Artificial Intelligence

2

Chapter 10 Artificial Intelligence

101 Intelligence and Machines 102 Understanding Images 103 Reasoning 104 Artificial Neural Networks 105 Genetic Algorithms 106 Other Areas of Research 107 Considering the Consequences

3

Intelligent agents

Agent = ldquodevicerdquo that responds to stimuli from its environment Sensorsmdashreceive data from environment --microphone camera Actuatorsmdashresponse to the stimuli --legs wings hands

The goal of artificial intelligence is to build agents that behave intelligently

4

Levels of intelligence in behavior

Reflex actions are predetermined responses to the input data

Intelligent response actions affected by knowledge of the environment

Goal seeking Learning

5

Artificial intelligence research approaches

Agentrsquos intelligence is judged by observing its input-response patterns Performance oriented Researcher

tries to maximize the performance of the agents

Simulation oriented Researcher tries to understand how the agents produce responses

6

Turing test

Proposed by Alan Turing in 1950 Benchmark for progress in artificial

intelligence Test setup Human interrogator

communicates with test subject by typewriter

Test Can the human interrogator distinguish whether the test subject is human or machine

7

Figure 101 The eight-puzzle in its solved configuration

8

Figure 102 Our puzzle-solving machine

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

2

Chapter 10 Artificial Intelligence

101 Intelligence and Machines 102 Understanding Images 103 Reasoning 104 Artificial Neural Networks 105 Genetic Algorithms 106 Other Areas of Research 107 Considering the Consequences

3

Intelligent agents

Agent = ldquodevicerdquo that responds to stimuli from its environment Sensorsmdashreceive data from environment --microphone camera Actuatorsmdashresponse to the stimuli --legs wings hands

The goal of artificial intelligence is to build agents that behave intelligently

4

Levels of intelligence in behavior

Reflex actions are predetermined responses to the input data

Intelligent response actions affected by knowledge of the environment

Goal seeking Learning

5

Artificial intelligence research approaches

Agentrsquos intelligence is judged by observing its input-response patterns Performance oriented Researcher

tries to maximize the performance of the agents

Simulation oriented Researcher tries to understand how the agents produce responses

6

Turing test

Proposed by Alan Turing in 1950 Benchmark for progress in artificial

intelligence Test setup Human interrogator

communicates with test subject by typewriter

Test Can the human interrogator distinguish whether the test subject is human or machine

7

Figure 101 The eight-puzzle in its solved configuration

8

Figure 102 Our puzzle-solving machine

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

3

Intelligent agents

Agent = ldquodevicerdquo that responds to stimuli from its environment Sensorsmdashreceive data from environment --microphone camera Actuatorsmdashresponse to the stimuli --legs wings hands

The goal of artificial intelligence is to build agents that behave intelligently

4

Levels of intelligence in behavior

Reflex actions are predetermined responses to the input data

Intelligent response actions affected by knowledge of the environment

Goal seeking Learning

5

Artificial intelligence research approaches

Agentrsquos intelligence is judged by observing its input-response patterns Performance oriented Researcher

tries to maximize the performance of the agents

Simulation oriented Researcher tries to understand how the agents produce responses

6

Turing test

Proposed by Alan Turing in 1950 Benchmark for progress in artificial

intelligence Test setup Human interrogator

communicates with test subject by typewriter

Test Can the human interrogator distinguish whether the test subject is human or machine

7

Figure 101 The eight-puzzle in its solved configuration

8

Figure 102 Our puzzle-solving machine

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

4

Levels of intelligence in behavior

Reflex actions are predetermined responses to the input data

Intelligent response actions affected by knowledge of the environment

Goal seeking Learning

5

Artificial intelligence research approaches

Agentrsquos intelligence is judged by observing its input-response patterns Performance oriented Researcher

tries to maximize the performance of the agents

Simulation oriented Researcher tries to understand how the agents produce responses

6

Turing test

Proposed by Alan Turing in 1950 Benchmark for progress in artificial

intelligence Test setup Human interrogator

communicates with test subject by typewriter

Test Can the human interrogator distinguish whether the test subject is human or machine

7

Figure 101 The eight-puzzle in its solved configuration

8

Figure 102 Our puzzle-solving machine

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

5

Artificial intelligence research approaches

Agentrsquos intelligence is judged by observing its input-response patterns Performance oriented Researcher

tries to maximize the performance of the agents

Simulation oriented Researcher tries to understand how the agents produce responses

6

Turing test

Proposed by Alan Turing in 1950 Benchmark for progress in artificial

intelligence Test setup Human interrogator

communicates with test subject by typewriter

Test Can the human interrogator distinguish whether the test subject is human or machine

7

Figure 101 The eight-puzzle in its solved configuration

8

Figure 102 Our puzzle-solving machine

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

6

Turing test

Proposed by Alan Turing in 1950 Benchmark for progress in artificial

intelligence Test setup Human interrogator

communicates with test subject by typewriter

Test Can the human interrogator distinguish whether the test subject is human or machine

7

Figure 101 The eight-puzzle in its solved configuration

8

Figure 102 Our puzzle-solving machine

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

7

Figure 101 The eight-puzzle in its solved configuration

8

Figure 102 Our puzzle-solving machine

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

8

Figure 102 Our puzzle-solving machine

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

9

Techniques for understanding images

The first intelligent behavior required by the puzzle-solving machine is the extraction of information through a visual medium1048708

Unlike photographing an image the problem is to understand the image (Computer Vision) ndashthe ability to perceive1048708

Since the possible images are finite the machine can merely compare the different sections of the picture to prerecorded templates pixel by pixel and reveal the condition of the puzzle1048708

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

10

Techniques for understanding images

Optical readers apply the similar method for image recognition (hand-writing)1048708

A certain degree of uniformity(style size orientation non-overlapping hellip) is required1048708

The alternative is to first extract the geometric features(digit 1 a single vertical line) and make comparison in terms of these features

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

11

Techniques for understanding images

Template matching Image processing identify the

characteristics of the image1048708 Edge enhancement to clarify the

boundary (taking a derivative) Region(with common properties color

hellip) finding for identifying objects Smoothing(removing flawsnoises in

image)1048708

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

12

Techniques for understanding images

Image analysis identify the meaning of these characteristics1048708

-- It is to recognize partially obstructed objects from different perspectives1048708

-- First assumption of what the image might be is made (clue)1048708

-- Then associate the image components with the objects conjectured to exist

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

13

Reasoning

After deciphering the positions of tiles from visual image the remaining task is to move the tiles to reach the final state from the current state1048708

The eight-puzzle has many configurations such that explicitly hard-coded each case for problem solving is not generally feasible

Some algorithm is necessary to resolve the problem in a systematic way1048708

The machine will then ably make decisions draw conclusions and perform elementary reasoning activities

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

14

Components of production systemsA production system classifies the common

characteristics shared by a class of reasoning problems and has the following components

1 Collection of states Start or initial state Goal state

2 Collection of productions rules or moves Each production may have preconditions

3 Control system decides which production to apply next

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

15

Data processing for production systems

State graph = states productions and preconditions

A graph consists of nodes and arcs(arrows) connecting nodes1048708A state graph has nodes representing states and arrows representing rules1048708

The arc linking two nodes signifies two states can be shift to each other using the rule the absence of arcs implicitly indicates the preconditions are not met1048708The problem magnitude may be too large for explicitly showing the entire state graph1048708

Partial representation of the state graph will help understand the problem

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

16

Data processing for production systems

State graph = states productions and preconditions

Search tree = record of state transitions explored while searching for a goal state Breadth-first search Depth-first search

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

17

Figure 103 A small portion of the eight-puzzlersquos state graph

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

18

Figure 104 Deductive reasoning in the context of a production system

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

19

Figure 105 An unsolved eight-puzzle

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

20

Figure 106 A sample search tree

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

21

Figure 107 Productions stacked for later execution

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

22

Figure 108 An unsolved eight-puzzle

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

23

Heuristic strategies

Generally a search tree may grow much huger if the nodesrsquo fan-outs are large 1048708

It becomes more complicate if the goal is very far away (more generations)1048708

Developing a full (exhaustive) search tree[brute-force methods] may be impractical1048708

In contrast to this breadth-first approach (layer by layer) we (humans) may attempt to pursue the more promising paths to greater depths in a depth-first manner (vertical)1048708

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

24

Heuristic strategies

Heuristic strategy is to develop a heuristicndasha quantitative measure on how close a state is to the goal

Requirements for good heuristics Must be much easier to compute than a

complete solution Must provide a reasonable estimate of

proximity to a goal

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

25

Figure 109 An algorithm for a control system using heuristics

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

26

Figure 1010 The beginnings of our heuristic search

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

27

Figure 1011 The search tree after two passes

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

28

Figure 1012 The search tree after three passes

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

29

Figure 1013 The complete search tree formed by our heuristic system

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

30

Neural networks

CPU is not capable of perceive and reasoning

Artificial neuron Each input is multiplied by a weighting

factor Output is 1 if sum of weighted inputs

exceeds a threshold value 0 otherwise Network is programmed by adjusting

weights using feedback from examples

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

31

Figure 1014 A neuron in a living biological system

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

32

Neural networks

ANN are multi-processing architectures to model networks of concurrent neurons1048708Each processing unit in ANN is a simple device to simulate the neuron1048708

The output of the unit may be 0 or 1 (or the fractional numbers in-between) dependent on the whether its effective input exceeds a given threshold value

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

33

Figure 1015 The activities within a processing unit

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

34

Figure 1016 Representation of a processing unit

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

35

Figure 1017 A neural network with two different programs (a) o=1 when 2 inputs diff(b)o=1 when both I=1

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

36

Neural networks

Different weights determine different output values1048708Case (a) will produce 1 if its two inputs differ while (b) outputs 1 if both inputs are 1rsquos1048708

A human brain contains roughly 10^11 neurons with about 10^4 synapses per neuron

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

37

character recognition

A specific application ndashcharacter recognition distinguish C and T regardless of the orientation1048708The network produces a 0 if the recognized letter is C or a 1 if the letter is a T

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

38

Figure 1018 Uppercase C and uppercase T

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

39

Figure 1019 Various orientations of the letters C and T

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

40

Neural networks

The system contains two levels of units1048708The first level has many units one for each 3x3 block of pixels1048708

Each unit has nine inputs the inputs of adjacent units overlap1048708Threshold = 5 centerrsquos weight = 2 othersrsquoweights = -11048708

The second level only has one unit with a separate input for each unit in the first level1048708Threshold = 5 each weight = 11048708

It outputs a 1 iff at least one input is a 1

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

41

Neural networks

If ldquoCrdquois present all the first level units will produce a 01048708All the possible cases can be enumerated

If ldquoTrdquois present only the first levelrsquos unit (highlighted below) will output a 1 while others output 0rsquos1048708The final output is 1

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

42

Figure 1020 The structure of the character recognition system

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

43

Figure 1021 The letter C in the field of view

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

44

Figure 1022 The letter T in the field of view

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

45

Associative memory

Associative memory = the retrieval of information relevant to the information at hand

One direction of research seeks to build associative memory using neural networks that when given a partial pattern transition themselves to a completed pattern

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

46

Figure 1023 An artificial neural network implementing an associative memory

1 The lines connecting circles are two-way connectionie output of one unit is connectedAs input of other unit2 The number associated with Lines are weights3 The number inside the circle is threshold

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

47

Figure 1024 The steps leading to a stable configuration

Two stable states1 Perimeter stable state (later stable state)When we initialize the Network with a least four Adjacent units on the Perimeter in their excitedstates

-11

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

48

The steps leading to a stable configuration

Two stable states2 Center stable state (former stable state)When we initialize the Network with center excited And no more than two of Perimeter in their excitedstates

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

49

Genetic algorithms

Simulate genetic processes to evolve algorithms Start with an initial population of ldquopartial

solutionsrdquo Graft together parts of the best performers

to form a new population Periodically make slight modifications to

some members of the current population Repeat until a satisfactory solution is

obtained

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

50

Figure 1025 Crossing two poker-playing strategies

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

51

Figure 1026 Coding the topology of an artificial neural network

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

52

Language processing

Syntactic analysis(subjectverb noun) Semantic analysis(identify actions) Contextual analysis(understanding)--The bat flew from his handEntire database Information retrieval(web searching) Information extraction(template)

Semantic net(a large linked data structure)

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

53

Figure 1027 A semantic net

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

54

Robotics

Began as a field within mechanical and electrical engineering

Today encompasses a much wider range of activities Robot cup competition Evolutionary robotics

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

55

Expert systems

Expert system = software package to assist humans in situations where expert knowledge is required Example medical diagnosis Often similar to a production system Blackboard model several problem-

solving systems share a common data area

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans

56

Some issues raised by artificial intelligence

When should a computerrsquos decision be trusted over a humanrsquos

If a computer can do a job better than a human when should a human do the job anyway

What would be the social impact if computer ldquointelligencerdquo surpasses that of many humans