cs 416 artificial intelligence
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CS 416 Artificial Intelligence. Lecture 1 Introduction. I Cannot Add Students to Course. Unfortunately, this class is oversubscribed I cannot add new students to the course Potential exception for 4 th -year CS Majors Feel free to stay through end of course today. Textbook. - PowerPoint PPT PresentationTRANSCRIPT
CS 416Artificial Intelligence
Lecture 1Lecture 1
IntroductionIntroduction
Lecture 1Lecture 1
IntroductionIntroduction
I Cannot Add Students to Course
Unfortunately, this class is oversubscribedUnfortunately, this class is oversubscribed
I cannot add new students to the courseI cannot add new students to the course
• Potential exception for 4Potential exception for 4thth-year CS Majors-year CS Majors
Feel free to stay through end of course todayFeel free to stay through end of course today
Unfortunately, this class is oversubscribedUnfortunately, this class is oversubscribed
I cannot add new students to the courseI cannot add new students to the course
• Potential exception for 4Potential exception for 4thth-year CS Majors-year CS Majors
Feel free to stay through end of course todayFeel free to stay through end of course today
Textbook
This is a great bookThis is a great book• 22ndnd edition released one month ago edition released one month ago
• Most widely used in U.S. universitiesMost widely used in U.S. universities
• It’s so good….It’s so good….
– I’m going to make you read it!I’m going to make you read it!
HomeworkHomework• Read chapters 1 and 2Read chapters 1 and 2
This is a great bookThis is a great book• 22ndnd edition released one month ago edition released one month ago
• Most widely used in U.S. universitiesMost widely used in U.S. universities
• It’s so good….It’s so good….
– I’m going to make you read it!I’m going to make you read it!
HomeworkHomework• Read chapters 1 and 2Read chapters 1 and 2
Syllabus
InstructorInstructor
• David BroganDavid BroganOlsson 217Olsson [email protected]@cs.virginia.edu
– Office hours: Wednesday 1:30 – 3:00Office hours: Wednesday 1:30 – 3:00
TATA
• Ben HockingBen Hocking
– Office hours: TBAOffice hours: TBA
InstructorInstructor
• David BroganDavid BroganOlsson 217Olsson [email protected]@cs.virginia.edu
– Office hours: Wednesday 1:30 – 3:00Office hours: Wednesday 1:30 – 3:00
TATA
• Ben HockingBen Hocking
– Office hours: TBAOffice hours: TBA
Syllabus
Class web page:Class web page:
• Soon to be at: http://www.cs.virginia.edu/~cs416Soon to be at: http://www.cs.virginia.edu/~cs416
GradingGrading
• 3 (perhaps 4) programming assignments (40%)3 (perhaps 4) programming assignments (40%)
• A couple homework assignments (10%)A couple homework assignments (10%)
• Midterm and Final (25% for each)Midterm and Final (25% for each)
Class web page:Class web page:
• Soon to be at: http://www.cs.virginia.edu/~cs416Soon to be at: http://www.cs.virginia.edu/~cs416
GradingGrading
• 3 (perhaps 4) programming assignments (40%)3 (perhaps 4) programming assignments (40%)
• A couple homework assignments (10%)A couple homework assignments (10%)
• Midterm and Final (25% for each)Midterm and Final (25% for each)
What is AI?
Discussion exercise for classDiscussion exercise for class
• Think of example AI systems (applications that are intelligent)Think of example AI systems (applications that are intelligent)
• Think of example AI TechniquesThink of example AI Techniques
Discussion exercise for classDiscussion exercise for class
• Think of example AI systems (applications that are intelligent)Think of example AI systems (applications that are intelligent)
• Think of example AI TechniquesThink of example AI Techniques
AI Systems
• ThermostatThermostat
• Tic-Tac-ToeTic-Tac-Toe
• Your carYour car
• ChessChess
• GoogleGoogle
• BabblefishBabblefish
• ThermostatThermostat
• Tic-Tac-ToeTic-Tac-Toe
• Your carYour car
• ChessChess
• GoogleGoogle
• BabblefishBabblefish• This thingThis thing
– AsimoAsimo
• This thingThis thing
– AsimoAsimo
AI Techniques
• Rule-basedRule-based
• Fuzzy LogicFuzzy Logic
• Neural NetworksNeural Networks
• Genetic AlgorithmsGenetic Algorithms
• Rule-basedRule-based
• Fuzzy LogicFuzzy Logic
• Neural NetworksNeural Networks
• Genetic AlgorithmsGenetic Algorithms
How to Categorize These Systems
Systems that think like humansSystems that think like humans
Systems that act like humansSystems that act like humans
Systems that think rationallySystems that think rationally
Systems that act rationallySystems that act rationally
Systems that think like humansSystems that think like humans
Systems that act like humansSystems that act like humans
Systems that think rationallySystems that think rationally
Systems that act rationallySystems that act rationally
Distinctions
How one thinks vs. How one actsHow one thinks vs. How one acts
• How can I know how you think?How can I know how you think?
– For the most part, you are a “black box”For the most part, you are a “black box”
– Cognitive ScienceCognitive Science
• How can I know how you act?How can I know how you act?
– ObservationObservation
– Turing TestTuring Test
How one thinks vs. How one actsHow one thinks vs. How one acts
• How can I know how you think?How can I know how you think?
– For the most part, you are a “black box”For the most part, you are a “black box”
– Cognitive ScienceCognitive Science
• How can I know how you act?How can I know how you act?
– ObservationObservation
– Turing TestTuring Test
Alan Turing – “Building a Brain”
World War II motivated computer advancesWorld War II motivated computer advances
• Code breaking (Colossus)Code breaking (Colossus)
• Computing missile trajectories (Mark I)Computing missile trajectories (Mark I)
• Electronic Numerical Integrator and Computer (ENIAC)Electronic Numerical Integrator and Computer (ENIAC)
Turing greatly involved with British efforts to build Turing greatly involved with British efforts to build computers and crack codes (Bletchley Park)computers and crack codes (Bletchley Park)
• Arrested for being a homosexual in 1952 and denied security clearanceArrested for being a homosexual in 1952 and denied security clearance
• Committed suicide in 1954Committed suicide in 1954
World War II motivated computer advancesWorld War II motivated computer advances
• Code breaking (Colossus)Code breaking (Colossus)
• Computing missile trajectories (Mark I)Computing missile trajectories (Mark I)
• Electronic Numerical Integrator and Computer (ENIAC)Electronic Numerical Integrator and Computer (ENIAC)
Turing greatly involved with British efforts to build Turing greatly involved with British efforts to build computers and crack codes (Bletchley Park)computers and crack codes (Bletchley Park)
• Arrested for being a homosexual in 1952 and denied security clearanceArrested for being a homosexual in 1952 and denied security clearance
• Committed suicide in 1954Committed suicide in 1954
Rational vs. Human
Thinking/acting rationally vs. Thinking/acting rationally vs. Thinking/acting like a human Thinking/acting like a human
• Rely on logic rather than human to measure correctnessRely on logic rather than human to measure correctness
• Thinking rationally (logically)Thinking rationally (logically)
– Socrates is a human; All humans are mortal; Socrates is mortalSocrates is a human; All humans are mortal; Socrates is mortal
– Logic formulas for synthesizing outcomesLogic formulas for synthesizing outcomes
• Acting rationally (logically)Acting rationally (logically)
– Even if method is illogical, the observed behavior must be rationalEven if method is illogical, the observed behavior must be rational
Thinking/acting rationally vs. Thinking/acting rationally vs. Thinking/acting like a human Thinking/acting like a human
• Rely on logic rather than human to measure correctnessRely on logic rather than human to measure correctness
• Thinking rationally (logically)Thinking rationally (logically)
– Socrates is a human; All humans are mortal; Socrates is mortalSocrates is a human; All humans are mortal; Socrates is mortal
– Logic formulas for synthesizing outcomesLogic formulas for synthesizing outcomes
• Acting rationally (logically)Acting rationally (logically)
– Even if method is illogical, the observed behavior must be rationalEven if method is illogical, the observed behavior must be rational
Perspective of this Course
We will investigate the general principles of We will investigate the general principles of rational agentsrational agents
• Not restricted to human actions and human environmentsNot restricted to human actions and human environments
• Not restricted to human thoughtNot restricted to human thought
• Not confined to only using laws of logicNot confined to only using laws of logic
• Anything goes so long as it produces rational behaviorAnything goes so long as it produces rational behavior
We will investigate the general principles of We will investigate the general principles of rational agentsrational agents
• Not restricted to human actions and human environmentsNot restricted to human actions and human environments
• Not restricted to human thoughtNot restricted to human thought
• Not confined to only using laws of logicNot confined to only using laws of logic
• Anything goes so long as it produces rational behaviorAnything goes so long as it produces rational behavior
What is AI?
The use of computers to solve problems that The use of computers to solve problems that previously could only be solved by applying human previously could only be solved by applying human intelligence…. thus something can fit this definition intelligence…. thus something can fit this definition today, but, once we see how the program works and today, but, once we see how the program works and understand the problem, we will not think of it as AI understand the problem, we will not think of it as AI anymoreanymore (David Parnas) (David Parnas)
The use of computers to solve problems that The use of computers to solve problems that previously could only be solved by applying human previously could only be solved by applying human intelligence…. thus something can fit this definition intelligence…. thus something can fit this definition today, but, once we see how the program works and today, but, once we see how the program works and understand the problem, we will not think of it as AI understand the problem, we will not think of it as AI anymoreanymore (David Parnas) (David Parnas)
Foundations - Philosophy
• Aristotle (384 B.C.E.) – Author of logical syllogismsAristotle (384 B.C.E.) – Author of logical syllogisms
• da Vinci (1452) – designed, but didn’t build, first mechanical da Vinci (1452) – designed, but didn’t build, first mechanical calculatorcalculator
• Descartes (1596) – can human free will be captured by a Descartes (1596) – can human free will be captured by a machine? Is animal behavior more mechanistic?machine? Is animal behavior more mechanistic?
• Necessary connection between logic and action is Necessary connection between logic and action is discovereddiscovered
• Aristotle (384 B.C.E.) – Author of logical syllogismsAristotle (384 B.C.E.) – Author of logical syllogisms
• da Vinci (1452) – designed, but didn’t build, first mechanical da Vinci (1452) – designed, but didn’t build, first mechanical calculatorcalculator
• Descartes (1596) – can human free will be captured by a Descartes (1596) – can human free will be captured by a machine? Is animal behavior more mechanistic?machine? Is animal behavior more mechanistic?
• Necessary connection between logic and action is Necessary connection between logic and action is discovereddiscovered
Foundations - Mathematics• More formal logical methodsMore formal logical methods
– Boolean logic (Boole, 1847)Boolean logic (Boole, 1847)
• Analysis of limits to what can be computedAnalysis of limits to what can be computed
– Intractability (1965) – time required to solve problem scales Intractability (1965) – time required to solve problem scales exponentially with the size of problem instanceexponentially with the size of problem instance
– NP-complete (1971) – Formal classification of problems as NP-complete (1971) – Formal classification of problems as intractableintractable
• Uncertainty (Cardano 1501)Uncertainty (Cardano 1501)
– The basis for most modern approaches to AIThe basis for most modern approaches to AI
– Uncertainty can still be used in logical analysesUncertainty can still be used in logical analyses
• More formal logical methodsMore formal logical methods
– Boolean logic (Boole, 1847)Boolean logic (Boole, 1847)
• Analysis of limits to what can be computedAnalysis of limits to what can be computed
– Intractability (1965) – time required to solve problem scales Intractability (1965) – time required to solve problem scales exponentially with the size of problem instanceexponentially with the size of problem instance
– NP-complete (1971) – Formal classification of problems as NP-complete (1971) – Formal classification of problems as intractableintractable
• Uncertainty (Cardano 1501)Uncertainty (Cardano 1501)
– The basis for most modern approaches to AIThe basis for most modern approaches to AI
– Uncertainty can still be used in logical analysesUncertainty can still be used in logical analyses
Foundations - Economics
• Humans are peculiar so define generic happiness term: Humans are peculiar so define generic happiness term: utilityutility
• Game Theory – study of rational behavior in small gamesGame Theory – study of rational behavior in small games
• Operations Research – study of rational behavior in Operations Research – study of rational behavior in complex systemscomplex systems
• Herbert Simon (1916 – 2001) – AI researcher who received Herbert Simon (1916 – 2001) – AI researcher who received Nobel Prize in Economics for showing people accomplish Nobel Prize in Economics for showing people accomplish satisficingsatisficing solutions, those that are good enough solutions, those that are good enough
• Humans are peculiar so define generic happiness term: Humans are peculiar so define generic happiness term: utilityutility
• Game Theory – study of rational behavior in small gamesGame Theory – study of rational behavior in small games
• Operations Research – study of rational behavior in Operations Research – study of rational behavior in complex systemscomplex systems
• Herbert Simon (1916 – 2001) – AI researcher who received Herbert Simon (1916 – 2001) – AI researcher who received Nobel Prize in Economics for showing people accomplish Nobel Prize in Economics for showing people accomplish satisficingsatisficing solutions, those that are good enough solutions, those that are good enough
Foundations - NeuroscienceHow do brains work?How do brains work?
• Early studies (1824) relied on injured and abnormal people to understand what Early studies (1824) relied on injured and abnormal people to understand what parts of brain doparts of brain do
• More recent studies use accurate sensors to correlate brain activity to human More recent studies use accurate sensors to correlate brain activity to human thoughtthought
– By monitoring individual neurons, monkeys can now control a computer By monitoring individual neurons, monkeys can now control a computer mouse using thought alonemouse using thought alone
• Moore’s law states computers will have as many gates as humans have Moore’s law states computers will have as many gates as humans have neurons in 2020neurons in 2020
• How close are we to having a mechanical brain?How close are we to having a mechanical brain?
– Parallel computation, remapping, interconnections, binary vs. gradient…Parallel computation, remapping, interconnections, binary vs. gradient…
How do brains work?How do brains work?
• Early studies (1824) relied on injured and abnormal people to understand what Early studies (1824) relied on injured and abnormal people to understand what parts of brain doparts of brain do
• More recent studies use accurate sensors to correlate brain activity to human More recent studies use accurate sensors to correlate brain activity to human thoughtthought
– By monitoring individual neurons, monkeys can now control a computer By monitoring individual neurons, monkeys can now control a computer mouse using thought alonemouse using thought alone
• Moore’s law states computers will have as many gates as humans have Moore’s law states computers will have as many gates as humans have neurons in 2020neurons in 2020
• How close are we to having a mechanical brain?How close are we to having a mechanical brain?
– Parallel computation, remapping, interconnections, binary vs. gradient…Parallel computation, remapping, interconnections, binary vs. gradient…
Foundations - Psychology
• Helmholtz and Wundt (1821) – started to make psychology a Helmholtz and Wundt (1821) – started to make psychology a science by carefully controlling experimentsscience by carefully controlling experiments
• The brain processes information (1842)The brain processes information (1842)
– stimulus converted into mental representationstimulus converted into mental representation
– cognitive processes manipulate representation to build cognitive processes manipulate representation to build new representationsnew representations
– new representations are used to generate actionsnew representations are used to generate actions
• Cognitive science started at a MIT workshop in 1956 with the Cognitive science started at a MIT workshop in 1956 with the publication three very influential paperspublication three very influential papers
• Helmholtz and Wundt (1821) – started to make psychology a Helmholtz and Wundt (1821) – started to make psychology a science by carefully controlling experimentsscience by carefully controlling experiments
• The brain processes information (1842)The brain processes information (1842)
– stimulus converted into mental representationstimulus converted into mental representation
– cognitive processes manipulate representation to build cognitive processes manipulate representation to build new representationsnew representations
– new representations are used to generate actionsnew representations are used to generate actions
• Cognitive science started at a MIT workshop in 1956 with the Cognitive science started at a MIT workshop in 1956 with the publication three very influential paperspublication three very influential papers
Foundations – Control Theory
• Machines can modify their behavior in response to the Machines can modify their behavior in response to the environment (sense / action loop)environment (sense / action loop)
– Water-flow regulator (250 B.C.E), steam engine governor, Water-flow regulator (250 B.C.E), steam engine governor, thermostatthermostat
• The theory of stable feedback systems (1894)The theory of stable feedback systems (1894)
– Build systems that transition from initialBuild systems that transition from initialstate to goal state with minimum energystate to goal state with minimum energy
– In 1950, control theory could only describeIn 1950, control theory could only describelinear systems and AI largely rose as alinear systems and AI largely rose as aresponse to this shortcomingresponse to this shortcoming
• Machines can modify their behavior in response to the Machines can modify their behavior in response to the environment (sense / action loop)environment (sense / action loop)
– Water-flow regulator (250 B.C.E), steam engine governor, Water-flow regulator (250 B.C.E), steam engine governor, thermostatthermostat
• The theory of stable feedback systems (1894)The theory of stable feedback systems (1894)
– Build systems that transition from initialBuild systems that transition from initialstate to goal state with minimum energystate to goal state with minimum energy
– In 1950, control theory could only describeIn 1950, control theory could only describelinear systems and AI largely rose as alinear systems and AI largely rose as aresponse to this shortcomingresponse to this shortcoming
Foundations - Linguistics
Speech demonstrates so much of human Speech demonstrates so much of human intelligenceintelligence
• Analysis of human language reveals thought taking place in Analysis of human language reveals thought taking place in ways not understood in other settingsways not understood in other settings
– Children can create sentences they have never heard Children can create sentences they have never heard beforebefore
– Language and thought are believed to be tightly Language and thought are believed to be tightly intertwinedintertwined
Speech demonstrates so much of human Speech demonstrates so much of human intelligenceintelligence
• Analysis of human language reveals thought taking place in Analysis of human language reveals thought taking place in ways not understood in other settingsways not understood in other settings
– Children can create sentences they have never heard Children can create sentences they have never heard beforebefore
– Language and thought are believed to be tightly Language and thought are believed to be tightly intertwinedintertwined
History of AI
Read the complete story in textRead the complete story in text• Alan Turing (1950) did much to define the problems and Alan Turing (1950) did much to define the problems and
techniquestechniques
• John McCarthy helped coordinate the players (1956)John McCarthy helped coordinate the players (1956)
• Alan Newell and Herbert Simon (1956) did much to Alan Newell and Herbert Simon (1956) did much to demonstrate first solutionsdemonstrate first solutions
• Marvin Minsky (student of von Neumann) built a neural Marvin Minsky (student of von Neumann) built a neural network (1951) from 3000 vacuum tubes and the “autopilot” network (1951) from 3000 vacuum tubes and the “autopilot” from a B-24 bomberfrom a B-24 bomber
Read the complete story in textRead the complete story in text• Alan Turing (1950) did much to define the problems and Alan Turing (1950) did much to define the problems and
techniquestechniques
• John McCarthy helped coordinate the players (1956)John McCarthy helped coordinate the players (1956)
• Alan Newell and Herbert Simon (1956) did much to Alan Newell and Herbert Simon (1956) did much to demonstrate first solutionsdemonstrate first solutions
• Marvin Minsky (student of von Neumann) built a neural Marvin Minsky (student of von Neumann) built a neural network (1951) from 3000 vacuum tubes and the “autopilot” network (1951) from 3000 vacuum tubes and the “autopilot” from a B-24 bomberfrom a B-24 bomber
Why is AI in Computer Science?
• Uses computer as a tool more than psychologists, Uses computer as a tool more than psychologists, mathematicians (operations research), or mechanical mathematicians (operations research), or mechanical engineers (control theory)engineers (control theory)
• Uses computer as a tool more than psychologists, Uses computer as a tool more than psychologists, mathematicians (operations research), or mechanical mathematicians (operations research), or mechanical engineers (control theory)engineers (control theory)
History of AI: 1952- 1969
Great successes!Great successes!
• Logic programs were replicating human logic in many casesLogic programs were replicating human logic in many cases
– Solving hard math problemsSolving hard math problems
– game playinggame playing
• LISP was invented by McCarthy (1958)LISP was invented by McCarthy (1958)
– second oldest language in existencesecond oldest language in existence
– could accept new axioms at runtimecould accept new axioms at runtime
• McCarthy went to MIT and Marvin Minsky started lab at StanfordMcCarthy went to MIT and Marvin Minsky started lab at Stanford
– Both powerhouses in AI to this dayBoth powerhouses in AI to this day
Great successes!Great successes!
• Logic programs were replicating human logic in many casesLogic programs were replicating human logic in many cases
– Solving hard math problemsSolving hard math problems
– game playinggame playing
• LISP was invented by McCarthy (1958)LISP was invented by McCarthy (1958)
– second oldest language in existencesecond oldest language in existence
– could accept new axioms at runtimecould accept new axioms at runtime
• McCarthy went to MIT and Marvin Minsky started lab at StanfordMcCarthy went to MIT and Marvin Minsky started lab at Stanford
– Both powerhouses in AI to this dayBoth powerhouses in AI to this day
History of AI: 1966 - 1973
A dose of reality – OverhypedA dose of reality – Overhyped
• Systems fail to play chess and translate RussianSystems fail to play chess and translate Russian
– Computers were ignorant to context of their logicComputers were ignorant to context of their logic
– Problems were intractableProblems were intractable
algorithms that work in principle may not work in practicealgorithms that work in principle may not work in practice
Combinatorial Explosion / Curse of DimensionalityCombinatorial Explosion / Curse of Dimensionality
– Fatal flaw in neural networks was exposedFatal flaw in neural networks was exposed
though flaw was first resolved in 1969, neural networks did not though flaw was first resolved in 1969, neural networks did not return to vogue until late 1980sreturn to vogue until late 1980s
A dose of reality – OverhypedA dose of reality – Overhyped
• Systems fail to play chess and translate RussianSystems fail to play chess and translate Russian
– Computers were ignorant to context of their logicComputers were ignorant to context of their logic
– Problems were intractableProblems were intractable
algorithms that work in principle may not work in practicealgorithms that work in principle may not work in practice
Combinatorial Explosion / Curse of DimensionalityCombinatorial Explosion / Curse of Dimensionality
– Fatal flaw in neural networks was exposedFatal flaw in neural networks was exposed
though flaw was first resolved in 1969, neural networks did not though flaw was first resolved in 1969, neural networks did not return to vogue until late 1980sreturn to vogue until late 1980s
AI History: 1969 - 1979
Knowledge-based SystemsKnowledge-based Systems• Previous systems knocked because general logical Previous systems knocked because general logical
algorithms could not be applied to realistic problemsalgorithms could not be applied to realistic problems
• Answer: accumulate specific logical algorithmsAnswer: accumulate specific logical algorithms
– DENDRAL – infer chemical structureDENDRAL – infer chemical structure
– knowledge of scientists boiled down to cookbook logicknowledge of scientists boiled down to cookbook logic
– large number of special purpose rules worked welllarge number of special purpose rules worked well
• Researchers work on ways to accumulate and store facts for Researchers work on ways to accumulate and store facts for expert systemsexpert systems
Knowledge-based SystemsKnowledge-based Systems• Previous systems knocked because general logical Previous systems knocked because general logical
algorithms could not be applied to realistic problemsalgorithms could not be applied to realistic problems
• Answer: accumulate specific logical algorithmsAnswer: accumulate specific logical algorithms
– DENDRAL – infer chemical structureDENDRAL – infer chemical structure
– knowledge of scientists boiled down to cookbook logicknowledge of scientists boiled down to cookbook logic
– large number of special purpose rules worked welllarge number of special purpose rules worked well
• Researchers work on ways to accumulate and store facts for Researchers work on ways to accumulate and store facts for expert systemsexpert systems
AI History: 1980 - present
Let the good times rollLet the good times roll
• The demonstrated success of AI invited investmentsThe demonstrated success of AI invited investments
• from millions to billions of dollars in 10 yearsfrom millions to billions of dollars in 10 years
• extravagant AI promises again led to “AI Winter” when extravagant AI promises again led to “AI Winter” when investments in technology dropped (1988)investments in technology dropped (1988)
Neural Networks come back from the dead (1986)Neural Networks come back from the dead (1986)
Let the good times rollLet the good times roll
• The demonstrated success of AI invited investmentsThe demonstrated success of AI invited investments
• from millions to billions of dollars in 10 yearsfrom millions to billions of dollars in 10 years
• extravagant AI promises again led to “AI Winter” when extravagant AI promises again led to “AI Winter” when investments in technology dropped (1988)investments in technology dropped (1988)
Neural Networks come back from the dead (1986)Neural Networks come back from the dead (1986)
AI History: 1987 - present
AI becomes a scienceAI becomes a science
• More repeatability of experimentsMore repeatability of experiments
• More development of mathematical underpinningsMore development of mathematical underpinnings
• Reuse of time-tested modelsReuse of time-tested models
Intelligent Agents (1994)Intelligent Agents (1994)
• AI systems exist in real environments with real sensory inputsAI systems exist in real environments with real sensory inputs
• Niches of AI need to be reorganizedNiches of AI need to be reorganized
AI becomes a scienceAI becomes a science
• More repeatability of experimentsMore repeatability of experiments
• More development of mathematical underpinningsMore development of mathematical underpinnings
• Reuse of time-tested modelsReuse of time-tested models
Intelligent Agents (1994)Intelligent Agents (1994)
• AI systems exist in real environments with real sensory inputsAI systems exist in real environments with real sensory inputs
• Niches of AI need to be reorganizedNiches of AI need to be reorganized
AI History: Where are We Now?
• Autonomous planning: scheduling operations aboard a Autonomous planning: scheduling operations aboard a spacecraftspacecraft
– Dante falls in an ice crater after one stepDante falls in an ice crater after one step
– Mars Rover never deploysMars Rover never deploys
• Game playing: Kasparov lost to IBM’s Big Blue in chessGame playing: Kasparov lost to IBM’s Big Blue in chess
– Rules were changed to prevent computer from retraining Rules were changed to prevent computer from retraining over night and to provide human players with more over night and to provide human players with more examples of computerized playexamples of computerized play
• Autonomous planning: scheduling operations aboard a Autonomous planning: scheduling operations aboard a spacecraftspacecraft
– Dante falls in an ice crater after one stepDante falls in an ice crater after one step
– Mars Rover never deploysMars Rover never deploys
• Game playing: Kasparov lost to IBM’s Big Blue in chessGame playing: Kasparov lost to IBM’s Big Blue in chess
– Rules were changed to prevent computer from retraining Rules were changed to prevent computer from retraining over night and to provide human players with more over night and to provide human players with more examples of computerized playexamples of computerized play
AI History: Where are We Now?
• Autonomous Control: CMU’s NAVLAB drove from Pittsburgh Autonomous Control: CMU’s NAVLAB drove from Pittsburgh to San Francisco under computer control 98% of timeto San Francisco under computer control 98% of time
• Logistics: deployment of troops to IraqLogistics: deployment of troops to Iraq
• Robotics: remote heart operationsRobotics: remote heart operations
• human genome, protein folding, drug discoveryhuman genome, protein folding, drug discovery
• stock marketstock market
• Autonomous Control: CMU’s NAVLAB drove from Pittsburgh Autonomous Control: CMU’s NAVLAB drove from Pittsburgh to San Francisco under computer control 98% of timeto San Francisco under computer control 98% of time
• Logistics: deployment of troops to IraqLogistics: deployment of troops to Iraq
• Robotics: remote heart operationsRobotics: remote heart operations
• human genome, protein folding, drug discoveryhuman genome, protein folding, drug discovery
• stock marketstock market