AI_chapter1

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Artificial IntelligenceAn Introductory Course


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Outline

1. Introduction

2. Problems and Search

3. Knowledge Representation

4. Advanced Topics

Game Playing

Uncertainty and Imprecision

Planning

Machine Learning


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References

Artificial Intelligence (1991)Elaine Rich & Kevin Knight, Second Ed, Tata McGraw Hill

Decision Support Systems and Intelligent SystemsTurban and Aronson, Sixth Ed, PHI


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Introduction

What is AI?

The foundations of AI

A brief history of AI

The state of the art

Introductory problems


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What is AI?


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What is AI?

Intelligence: ability to learn, understand andthink (Oxford dictionary)

AI is the study of how to make computers makethings which at the moment people do better.

Examples: Speech recognition, Smell, Face, Object,Intuition, Inferencing, Learning new skills, Decisionmaking, Abstract thinking


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What is AI?

Thinking humanly Thinking rationally

Acting humanly Acting rationally


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Acting Humanly: The Turing Test

Alan Turing (1912-1954)

Computing Machinery and Intelligence

(1950)

HumanInterrogator

Human

AISystem

Imitation Game


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Acting Humanly: The Turing Test

Predicted that by 2000, a machine might have a30% chance of fooling a lay person for 5 minutes.

Anticipated all major arguments against AI infollowing 50 years.

Suggested major components of AI: knowledge,reasoning, language, understanding, learning.


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Thinking Humanly: Cognitive Modelling

Not content to have a program correctly solving aproblem.

More concerned with comparing its reasoning stepsto traces of human solving the same problem.

Requires testable theories of the workings of thehuman mind: cognitive science.


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Thinking Rationally: Laws of Thought

Aristotle was one of the first to attempt to codifyright thinking, i.e., irrefutable reasoningprocesses.

Formal logic provides a precise notation and rulesfor representing and reasoning with all kinds ofthings in the world.

Obstacles: Informal knowledge representation.

Computational complexity and resources.


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Acting Rationally

Acting so as to achieve ones goals, givenones beliefs.

Does not necessarily involve thinking.

Advantages:More general than the laws of thought

approach. More amenable to scientific development than

human- based approaches.


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The Foundations of AI

Philosophy (423 BC present):Logic, methods of reasoning.

Mind as a physical system.

Foundations of learning, language, and rationality.

Mathematics (c.800 present):Formal representation and proof.

Algorithms, computation, decidability, tractability. Probability.


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The Foundations of AI

Psychology (1879 present):Adaptation.

Phenomena of perception and motor control.

Experimental techniques.

Linguistics (1957 present):Knowledge representation.

Grammar.


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A Brief History of AI

The gestation of AI (1943 1956):1943: McCulloch & Pitts: Boolean circuit model of brain.

1950: Turings Computing Machinery and Intelligence.

1956: McCarthys name Artificial Intelligence adopted.

Early enthusiasm, great expectations (1952 1969):Early successful AI programs: Samuels checkers,

Newell & Simons Logic Theorist, Gelernters Geometry

Theorem Prover.

Robinsons complete algorithm for logical reasoning.


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A dose of reality (1966 1974):AI discovered computational complexity.

Neural network research almost disappeared after

Minsky & Paperts book in 1969.

Knowledge-based systems (1969 1979):1969: DENDRAL by Buchanan et al..

1976: MYCIN by Shortliffle.

1979: PROSPECTOR by Duda et al..


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AI becomes an industry (1980 1988):Expert systems industry booms.

1981: Japans 10-year Fifth Generation project.

The return of NNs and novel AI (1986 present):Mid 80s: Back-propagation learning algorithm reinvented.

Expert systems industry busts.

1988: Resurgence of probability.

1988: Novel AI (ALife, GAs, Soft Computing, ). 1995: Agents everywhere.

2003: Human-level AI back on the agenda.


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Task Domains of AI Mundane Tasks:

Perception Vision Speech

Natural Languages Understanding Generation

Translation Common sense reasoning Robot Control

Formal Tasks Games : chess, checkers etc Mathematics: Geometry, logic,Proving properties of programs

Expert Tasks: Engineering ( Design, Fault finding, Manufacturing planning) Scientific Analysis Medical Diagnosis Financial Analysis


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AI Technique

Intelligence requires Knowledge

Knowledge posesses less desirable properties such as:

Voluminous

Hard to characterize accurately Constantly changing

Differs from data that can be used

AI technique is a method that exploits knowledge that shouldbe represented in such a way that:

Knowledge captures generalization

It can be understood by people who must provide it

It can be easily modified to correct errors.

It can be used in variety of situations


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The State of the Art

Computer beats human in a chess game.

Computer-human conversation using speech recognition.

Expert system controls a spacecraft.

Robot can walk on stairs and hold a cup of water.

Language translation for webpages.

Home appliances use fuzzy logic.

......


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Tic Tac Toe

Three programs are presented : Series increase

Their complexity

Use of generalization

Clarity of their knowledge

Extensability of their approach


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Introductory Problem: Tic-Tac-Toe

X X

o


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Comments:This program is very efficient in time.

1. A lot of space to store the Move-Table.

2. A lot of work to specify all the entries in the

Move-Table.

3. Difficult to extend.


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1 2 3

4 5 6

7 8 9


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Introductory Problem: Tic-Tac-ToeProgram 2:Data Structure: A nine element vector representing the board. But

instead of using 0,1 and 2 in each element, we store 2 for blank, 3for X and 5 for O

Functions:Make2: returns 5 if the center sqaure is blank. Else any other balnk sqPosswin(p): Returns 0 if the player p cannot win on his next move;

otherwise it returns the number of the square that constitutes awinning move. If the product is 18 (3x3x2), then X can win. If theproduct is 50 ( 5x5x2) then O can win.

Go(n): Makes a move in the square n

Strategy:

Turn = 1 Go(1)

Turn = 2 If Board[5] is blank, Go(5), else Go(1)Turn = 3 If Board[9] is blank, Go(9), else Go(3)Turn = 4 If Posswin(X) 0, then Go(Posswin(X)).......


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Comments:

1. Not efficient in time, as it has to check several

conditions before making each move.

2. Easier to understand the programs strategy.

3. Hard to generalize.


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8 3 4

1 5 9

6 7 215(8 +

5)


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Comments:

1. Checking for a possible win is quicker.

2. Human finds the row-scan approach easier, while

computer finds the number-counting approach more

efficient.


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Program 3:

1. If it is a win, give it the highest rating.

2. Otherwise, consider all the moves the opponent

could make next. Assume the opponent will make

the move that is worst for us. Assign the rating of

that move to the current node.

3. The best node is then the one with the highest

rating.


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Comments:

1. Require much more time to consider all possible

moves.

2. Could be extended to handle more complicated

games.


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Introductory Problem: Question

AnsweringMary went shopping for a new coat. She found a red

one she really liked. When she got it home, she

discovered that it went perfectly with her favourite

dress.

Q1: What did Mary go shopping for?

Q2: What did Mary find that she liked?

Q3: Did Mary buy anything?


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AnsweringProgram 1:

1. Match predefined templates to questions to generate

text patterns.

2. Match text patterns to input texts to get answers.

What did X Y What did Mary go shopping for?

Mary go shopping for Z

Z = a new coat


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AnsweringProgram 2:

Structured representation of sentences:

Event2: Thing1:

instance: Finding instance: Coat

tense: Past colour:Red

agent: Mary

object: Thing 1


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AnsweringProgram 3:

Background world knowledge:

C finds M

C leaves L C buys M

C leaves L

C takes M


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Exercises

1. Characterize the definitions of AI:

"The exciting new effort to make computers think ...machines with minds, in the full and literal senses"

(Haugeland, 1985)

"[The automation of] activities that we associate withhuman thinking, activities such as decision-making,problem solving, learning ..."(Bellman, 1978)


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Exercises

"The study of mental faculties, through the use ofcomputational models"(Charniak and McDermott, 1985)

"The study of the computations that make it possible toperceive, reason, and act"(Winston, 1992)

"The art of creating machines that perform functions thatrequire intelligence when performed by people"(Kurzweil, 1990)


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Exercises

"The study of how to make computers do things at which,at the moment, people are better"(Rich and Knight, 1991)

"A field of study that seeks to explain and emulateintelligent behavior in terms of computationl processes"(Schalkoff, 1990)

"The branch of computer science that is concerned withthe automation of intelligent behaviour"

(Luger and Stubblefield, 1993)


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Exercises

"A collection of algorithms that are computationallytractable, adequate approximations of intractabiliyspecified problems"(Partridge, 1991)

"The enterprise of constructing a physical symbolsystem that can reliably pass the Turing test"(Ginsberge, 1993)

"The f ield of computer science that studies howmachines can be made to act intelligently"(Jackson, 1986)

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