formal intro to ai

7/27/2019 Formal Intro to AI

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ARTIFICIAL INTELLIGENCE (AI)

A FORMAL INTRODUCTION


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What doesartificial intelligencemean?

Programming a computer to successfullyperform tasks that are thought to requireintelligence Playing chess

Proving theorems

Translating Russian into English Walking across a room

Recognizing a familiar face

Understanding directions


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DEFINITIONS OF AI

Study of how to make computers do things atwhich, at the moment, people are better.(Elain Rich) (Memorizing and intelligence)

The science of making machines do thingsthat would require intelligence if done byhuman. (Minsky)

Based on programming techniques: A branchof computer science dealing with symbolic,non-algorithmic methods of problem solving.(Buchanan and Shortliffe)


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Some more definitions

Part of computer science that concerns with

designing intelligent computer systems, that is,

systems that exhibit the characteristics we associate

with intelligence in human behavior. (Barr andFeigenbaum)

The branch of computer science that deals with ways

of representing knowledge using symbols rather thannumbers and with rules-of-thumb or heuristic

methods for processing information. (Buchanan)


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Some more definitions

A way of making a machine think intelligently.

Branch of computer science that is concernedwith automation of intelligent behavior.

A recursive definition: AI is the collection ofproblems and methodologies studied by AIresearchers.

HISTORY: 1930: John Dewey, How wethink to 1990sGenetic Algorithms andthe 6th Generation Computing


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HISTORY OF AIMain Events


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The Dartmouth Conference

According to the organizersproposal, the

conference was intended to explore theconjecture, that every aspect of learningor any other feature of intelligence can inprinciple be so precisely described that a

machine can be made to simulate it.That conjecture, of course, continues tobe a focus of AI research.


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The Conference Organizers John McCarthy - Assistant Professor of Mathematics

at Dartmouth. Marvin Minsky - Junior Fellow in mathematics and

neurology at Harvard, both had worked with

Shannon at Bell Labs. Nathaniel Rochester - Manager of information

research for IBM. Interested in intelligent machines. Claude Shannon - Bell Labs; Established his

reputation firmly earlier.

The conference, funded by a $7500Rockefeller Foundation Grant, wasorganized by four scientists, 2 fromacademia and 2 from industry.


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The Dartmouth Conference

1956 Dartmouth Conference, Hanover, NewHampshire:

AI revolution was launched. About a dozenscientists representing disciplines of Mathematics

Neurology

Psychology Electrical Engineering among others

werethere.


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The Dartmouth Conference They all in their various fields, were using

computers to try to simulate various aspects ofhuman intelligence.

Anew branch of computer science crystallized atthe conference, combining elements of severaldifferent avenues of research into a unified field.

There was no universal agreement about what tocall the new science.

However, Artificial Intelligence, the namesuggested by John McCarthy, one of theconference organizers, has come to beassociated firmly with the field.


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Can Machines Think?

Computers were used by Americans andthe British during World War II toexpedite complex tasks such asnumerical computations and code

breaking activities that previously hadbeen assumed to require humanintelligence.


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Can Machines Think? Notice that shift in the frontier of

intelligence in the last 50 years. Wehave become so accustomed tocalculating machines that we nowconsider that kind of activity to be

mechanical.

It was probably inevitable that scientistsworking with the first computers wouldspeculate about how intelligent thesenew electronic marvels could become.


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Can Machines Think? Alan Turing, a mathematician, was working on

Project Ultra, the successful British effort tobreak the German Code during World War II. Aspart if his role in that project, Turing helpeddesign one of the first computers ever built.

Turing also wrote an article entitledComputingMachinery and Intelligence, which secured forhim the distinction of being generally recognizedas the father ofAI. He proposed a question

Can machines think? Turing suggested a test, in the form of game,

that could help decide the issue. He called itimitation game. (Replicating, human behavior)


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The Turing Test You are the interrogator; you can direct

questions to either Person A or Person Bthrough a keyboard to the screen, but youdo not know which is the man and which is

the woman. Only one of the persons isobligated to reply truthfully, the otherperson is actively engaged in attempting tofool and confuse you, using any deceitfultactics that will make you guess incorrectly.

The objective of the game is to try to guesswhich person is male and which is femalesolely by analyzing the responses through akeyboard. Screen communication.


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The Turing Test Next, and this is the critical part of the

Turing test, substitute a computer for

one of the people. Now the human isobligated to give you truthful, human-likeresponses; but the computer is trying tofool you into thinking that it is human!


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The Turing Test It is considered to be any situation in

which a human converses with an

unseen respondent and attempts todetermine if the dialogue is beingconducted with a human or a computer.If a computer can fool you into believing

that you are talking to a human, thecomputer can be said to be intelligent.


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Intelligent Test for Computers Alan Turing, a pioneer in the theory of computation, once

proposed an intelligent test for computer programs[Turing 1950].

In one variant of the Turing Test, a human judge isallowed to interrogate a program through some sort of aninterface such as a video terminal. If the program can foolthe human into believing that it is another humanresponding rather than a computer, then the program isjudged intelligent.

You can imagine variants of this test in which youmanipulate a robots environment to see how robotresponds and judge the robot as intelligent or not,depending on whether the robot responds in accordancewith how a human might in the same situation.


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The Loebner Prize, based on a fund of over $100,000

established by New York businessman Hugh G. Loebner, isawarded annually for the computer program that bestemulates natural human behavior. During the contest, a panelof independent judges attempts to determine whether theresponses on a computer terminal are being produced by acomputer or a person, along the lines of the Turing Test. Thedesigners of the best program each year win a cash awardand a medal. If a program passes the test in all its particulars,then the entire fund will be paid to the program's designer andthe fund abolished. For further information about the LoebnerPrize, see the URL

http://www.loebner.net/Prizef/loebner-prize.html

What AI competitions exist?


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HISTORY OF AIDetails Later


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DISUCSSING NATURAL ANDARTIFICIAL INTELLIGENCE


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AI TREE


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Computers are

Good at:

Number crunching

Storing information

Airline scheduling

Transmitting data

Structured data bases

Graphics

Bad at:

Writing poetry

Composing music

Understanding

speech

Driving cars

Enjoying peaches Learning new things


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Artificial Intelligence

Human Intelligence!

We need to build intelligent machines?


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INTELLIGENCE

Natural God made

Associated with human

Human behavior Psychology

Five senses

Symbolic data

Fuzzy ness

Artificial Man made

Associated with machines

To emulate humanbehavior in terms ofcomputational processes

Various sensors

Numeric + Symbolic

Uncertainty to be dealtwith


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Definition ofIntelligent Agent:

A software routine that waits in the background

and performs an action when a specified eventoccurs.

For example, agents could transmit a summary fileon the first day of the month or monitor incoming

data and alert the user when certain transactionshave arrived.


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How do we build an intelligent agent?

Must be able to perceive its environment.

Must be able to affect its environment. Must be able to reason about observations and

actions

Must be able to learn from observations andactions.

Must have goals.


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Building Intelligent Agents:

First Challenge

Create a representation of the world in termscomputers can deal with

Numbers?

Text

Logic

Lets assume everything about the task can berepresented we have complete knowledge


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Building Intelligent Agents:Second Challenge

Extend your programs to handle situations whereknowledge isnt complete, i.e.,

where there is uncertainty


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prior knowledge

experience

goals/values

observations

Intelligent Agents


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Building Intelligent Agents

Agent

prior knowledge

actionsexperience

goals/values

observations


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Intelligent Agent Skills include

ReasoningSearch Machine Learning

Representation of the World

Symbols (Logic, Numbers)

Vision Processing

Planning

RoboticsNatural Language Understanding


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AI programming Vs.

Conventional programming

AI

Primarily symbolic

Heuristic, Solution stepsimplicit

Control structureseparate from knowledgebase

Easy to modify

Satisfactory answersacceptable

CONVENTIONAL

Primarily numeric

Algorithmic, Solutionstep explicit

Information andcontrol integrated

Difficult to modify

Correct answersrequired


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NLP

ROBOTS

& MV

ES

T.Prov.

Heuristic

Search

Knowledge

Rep.

Languages

& Tools

Reasoning

& Logic


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AI: CENTRAL THEMES

THEMES

K. representation

Inference and control

Learn and adapt Handling uncertainty

Reasoning

Knowledge/search

tradeoff, Combinatorialexplosion

Problem decomposition


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WHAT IS INTELLIGENCE AND HOW IT WORKS?

Intelligence is ability to meet situations successfully

perceive inter relationship of facts

learn and understand fromexperience

acquire and retain knowledge

respond quickly and successfully to anew situation

To respond to a situation very flexibly

To make sense out of ambiguous orcontradictory messages

To recognize the relative importanceof different elements of a situation

To find similarities B/W situationsdespite differences which mayseparate them

To draw distinctions B/W situationsdespite similarities which may link

them

Goals

We think because thereare things we have to

do

Intelligence

Facts and rules

Control

Pruning Inferencing


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Facts And Rules

Intelligence

1) Collection of facts.

2) Means of utilizing these facts to reach goals.

3) Done by formulating set of rules relating to allfacts stored in the brain.


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Fact/Rule Set1:

Fact 1: A burning stove is hot.

Rule 1:If I put my hand on a burning stove,

Then it hurt.

Fact/Rule Set 2:

Fact 2: During rush hours, streets are crowded with cars.

Rule 2: If I try to cross a major highway on foot during rush

hours,

Then I may get hit by a car.

How Does Human Intelligence Work?


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How Does Human Intelligence Work?

Goals:Example

Wake up in the morning.To go to office.

Reach in time.Goals ----- ultimate goals.

Thoughts are not random or arbitrary. Pressed into service because of goal(s). No thoughts without goal. We do not do things because we think, we think

because there are things we have to do.


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Example; Wakeup in the morning.

Reach office in time.

Brain is bombarded with all type of data .

(directly/indirectly , related/unrelated).

If we had to deal with this multi data,

we might stand across the road for years.

How mind extracts the right set of rules is pruning.

Eliminates irrelevant pathways.

Reach goal immediately.

Focus on rules pertinent to solving the immediate

problem.


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Reaching goal; Problem solved + New knowledge acquired.

Fact 1: Jims parents are John & Mary.

Fact 2: Janes parents are John & Mary.

Rule: If a male person & female person have same parents.

Then they are brother and sister.

Goals: Relation between Jim & Jane.

Inference: New facts produced;

Jim & Jane are brother and sister.

The component of intelligence that helps us arrive at new factsis called the Inference Engine, Inference Mechanism.

Problem may have not a vast store of information. From this we

have to determine the proper course of action. Avoid irrelevant

knowledge components. This is what we called Pruning

Interference Mechanism


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WHAT IS INTELLIGENCE AND HOW IT WORKS?

Intelligence is ability to

meet situations successfully perceive inter relationship of facts learn and understand from

experience acquire and retain knowledge respond quickly and successfully to a

new situation To respond to a situation very

flexibly To make sense out of ambiguous or

contradictory messages To recognize the relative importance

of different elements of a situation To find similarities B/W situations

despite differences which mayseparate them

To draw distinctions B/W situationsdespite similarities which may linkthem

Goals

We think becausethere are things wehave to do

Intelligence

Facts and rules Control

Pruning Inferencing


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Strong AIIt makes the bold claim that computers can be made to think ona level (at least) equal to humans.

Weak AIIt simply states that some thinking like features can be added

to computers make them more useful tools like expert

systems, speech recognition software etc.


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Intelligent Systems

ArtificialIntelligence

Systems

Statistics

SignalProcessing Control

Theory

TheoreticalPhysicsIntelligent


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Robots


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AITechn

ologies


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AI Technologies


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AI Technologies


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AI Technologies


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AI Technologies


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AI Technologies

Intelligent Computer Aided Instruction

Robotics

Expert Systems

Computer Vision

Speech Recognition

Automatic Programming

Natural Language Processing

Planning and Decision Support

AI Technologies and Applications


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AI Technologies and Applications

Factory

Automation

Autonomous

Vehicals

Mechatronics

ROBOTICS

Fault

Diagnosis

Intelligent

Assistant

Medical,

Control, etc.

EXPERT

SYSTEMS

Perception

Guidance

Inspection

Verification

VISION

Understanding

Generation

Text to

Speech

Speaker

SPEECH

Comm.

Grammer

NLU

Learning

and teaching

CAI GAME

PLAYING

GPS

T. PROVING

Neural

Networks

Parallel

Computing

Genetic

Algorithms

LEARNING

Acquisition

Text

understandin

Text

generation

Explanation

KE

AI TECHNOLOGIES

Domain Specific Applications of AI
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Domain Specific Applications of AI

Diagnosis

Treatment

Monitoring

Knowledge

Automation

MEDICAL

Plant

control

Data

interpretation

Intelligent

design

Chem.

& Bio.

Synthesis

SCIENCE

& ENGG.

Factory

M, P & S

Intelligent

Robots

Inspection

Mechatronics

INDUSTRY

Surveillance

Target

Trk. & Recog.

Autonomous

Vehicles

Expert

Advisors

MILITARY

Intelligent

KB Access

Traffic

control

SERVICES

Groupd

Ops. Aid

P & S

Remote

Ops.

Sensor

Fusion

SPACE

Tax Prep.

ES

Intelligent

Consultant

FINANCIAL

Prospecting

Aids

Drilling

Ops.

Resource

Recovery

Resource

Management

NATURAL

RESOURCES

APPLICATIONS
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Generic Applications of AIKnowledge Management:

Intelligent Databaseaccess

Knowledge acquisition

Text understanding

Text generation

Machine translation

Explanation

Logical operations ondatabases

Human Interaction:

Speech understanding

Speech generation

Learning and Teaching:

Computer aided instruction

Intelligent computer aidedinstruction

Learning from experience

Concept generation

Operation and maintenanceinstruction


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Generic Applications of AI

Fault Diagnosis andRepair:

Humans

Machines

Systems

Computation:

Symbolic Mathematics

Fuzzy operation

Automatic programming

Communication:

Public access to largedatabases via telephone andspeech understanding

Natural Language interfaces

to computer programs

Operations of Machines andComplex Systems:

Factory Automation

Mechatronics


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Autonomous IntelligentSystems:

Autonomous vehicles

Management:

Planning

Scheduling

Monitoring

Sensor Interpretation andIntegration:

Developing meaning fromsensor data

Sensor fusion (integratingmultiple sensor inputs todevelop high level

interpretations)


Design:

Systems

Equipment

Intelligent Design Aids

InventingVisual Perception andGuidance:

Inspection

Identification

Verification


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Visual Perception andGuidance (Continued):

Guidance

Screening

Monitoring

Intelligent Assistants:

Medical Diagnosis,Maintenance Aids, andother Expert Systems

Expert System BuildingTools


Medical:Patient Monitoring

Prosthetics

Artificial Sight andHearing

Reading Machinesfor the Blind

Medical KnowledgeAutomation

Executive Assistance:Real Mail and SpotItems on Importance

Planning Aids


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Science And Engineering:Discovering

Physical andmathematical laws

Determination ofregularities andaspects of interest

Chemical and BiologicalSynthesis Planning

Test Management

Data Interpretation

Intelligent Design Aids


Industrial:Factory Management

Production Planningand Scheduling

Intelligent Robots

Process Planning

Intelligent Machines

Computer-AidedInspection

Mechatronics


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Military:

Expert Advisers

Sensor Synthesisand Interpretation

Battle and ThreatAssessment

Automatic PhotoInterpretation

Tactical planning

Military

Surveillance

Weapon-TargetAssignment

AutonomousVehicles

Military:

Intelligent Robots

Diagnosis and Maintenance Aids

Target Location and Tracking

Map Development Aids

Intelligent Interactions withKnowledge Bases

Financial:

Tax Preparation

Financial Expert Systems

Intelligent Consultants



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International:

Aids to Understanding andInterpretation

Goals, aspirations andmotives of differentcountries and cultures

Cultural models forinterpreting how othersperceive

Natural language Translation

Services:

Intelligent Knowledge BaseAccess

Airline Reservations

Air Traffic Control and Ground

Traffic Control

Natural Resources:

Prospecting Aids

Resource operations

Drilling Procedures

Resource Recovery

GuidanceResource Management UsingRemote Sensing Data



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Space:

Ground Operation Aids

Planning and Scheduling Aids

Diagnosis and Reconfiguration Aids

Remote Operations of Spacecraft and SpaceVehicles

Test Monitors

Real-time Re-planning as required by failures,changed conditions, or new opportunities

Automatic Subsystem Operations



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A game of chess can be analyzed by a symbolicprogram. Each chess piece is represented by a symbol,and the rules governing legal moves are representedby symbolic expressions.

Playing Chess


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APPLYING AI Physics

VLSI Design

Non DestructiveTesting

Element

Classification Load Measuring

MechanicalDesign Assembly

TeachingPhysics

IsotopesDetection

Circuit Analysis

Learning

Control

Broom Balancer

Prediction andEstimation

Backing of aTruck

Power PlantControl

Robotics

PrecisionManufacturing

Computing

SpeechRecognition

Intelligent Agenton the Net

Network

Management andRouting

Troubleshooting

CharacterRecognition

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