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Artificial Intelligence

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Title: Artificial Intelligence


1
Artificial Intelligence
  • An Introductory Course

2
Outline
  • Introduction
  • Problems and Search
  • Knowledge Representation
  • Advanced Topics
  • - Game Playing
  • - Uncertainty and Imprecision
  • - Planning
  • - Machine Learning

3
References
  • Artificial Intelligence (1991)
  • Elaine Rich Kevin Knight, Second Ed, Tata
    McGraw Hill
  • Decision Support Systems and Intelligent Systems
  • Turban and Aronson, Sixth Ed, PHI

4
Introduction
  • What is AI?
  • The foundations of AI
  • A brief history of AI
  • The state of the art
  • Introductory problems

5
What is AI?
6
What is AI?
  • Intelligence ability to learn, understand and
    think (Oxford dictionary)
  • AI is the study of how to make computers make
    things which at the moment people do better.
  • Examples Speech recognition, Smell, Face,
    Object, Intuition, Inferencing, Learning new
    skills, Decision making, Abstract thinking

7
What is AI?
Thinking humanly Thinking rationally
Acting humanly Acting rationally
8
Acting Humanly The Turing Test
  • Alan Turing (1912-1954)
  • Computing Machinery and Intelligence (1950)

Imitation Game
Human
Human Interrogator
AI System
9
Acting Humanly The Turing Test
  • Predicted that by 2000, a machine might have a
    30 chance of fooling a lay person for 5 minutes.
  • Anticipated all major arguments against AI in
  • following 50 years.
  • Suggested major components of AI knowledge,
  • reasoning, language, understanding, learning.

10
Thinking Humanly Cognitive Modelling
  • Not content to have a program correctly solving a
    problem.
  • More concerned with comparing its reasoning
    steps
  • to traces of human solving the same problem.
  • Requires testable theories of the workings of the
  • human mind cognitive science.

11
Thinking Rationally Laws of Thought
  • Aristotle was one of the first to attempt to
    codify right thinking, i.e., irrefutable
    reasoning processes.
  • Formal logic provides a precise notation and
    rules for representing and reasoning with all
    kinds of things in the world.
  • Obstacles
  • - Informal knowledge representation.
  • - Computational complexity and resources.

12
Acting Rationally
  • Acting so as to achieve ones goals, given ones
    beliefs.
  • Does not necessarily involve thinking.
  • Advantages
  • - More general than the laws of thought
    approach.
  • - More amenable to scientific development than
    human- based approaches.

13
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.

14
The Foundations of AI
  • Psychology (1879 - present)
  • - Adaptation.
  • - Phenomena of perception and motor control.
  • - Experimental techniques.
  • Linguistics (1957 - present)
  • - Knowledge representation.
  • - Grammar.

15
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.

16
A Brief History of AI
  • 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..

17
A Brief History of AI
  • 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.

18
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

19
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 should be 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

20
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.
  • ......

21
Tic Tac Toe
  • Three programs are presented
  • Series increase
  • Their complexity
  • Use of generalization
  • Clarity of their knowledge
  • Extensability of their approach

22
Introductory Problem Tic-Tac-Toe
X X
o

23
Introductory Problem Tic-Tac-Toe
  • Program 1
  • Data Structures
  • Board 9 element vector representing the board,
    with 1-9 for each square. An element contains the
    value 0 if it is blank, 1 if it is filled by X,
    or 2 if it is filled with a O
  • Movetable A large vector of 19,683 elements (
    39), each element is 9-element vector.
  • Algorithm
  • 1. View the vector as a ternary number. Convert
    it to a
  • decimal number.
  • 2. Use the computed number as an index into
  • Move-Table and access the vector stored there.
  • 3. Set the new board to that vector.

24
Introductory Problem Tic-Tac-Toe
  • 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.

25
Introductory Problem Tic-Tac-Toe
1 2 3
4 5 6
7 8 9
26
Introductory Problem Tic-Tac-Toe
  • Program 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, 3
    for X and 5 for O
  • Functions
  • Make2 returns 5 if the center sqaure is blank.
    Else any other balnk sq
  • Posswin(p) Returns 0 if the player p cannot win
    on his next move otherwise it returns the number
    of the square that constitutes a winning move. If
    the product is 18 (3x3x2), then X can win. If
    the product is 50 ( 5x5x2) then O can win.
  • Go(n) Makes a move in the square n
  • Strategy
  • Turn 1 Go(1)
  • Turn 2 If Board5 is blank, Go(5), else Go(1)
  • Turn 3 If Board9 is blank, Go(9), else Go(3)
  • Turn 4 If Posswin(X) ? 0, then Go(Posswin(X))
  • .......

27
Introductory Problem Tic-Tac-Toe
  • 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.

28
Introductory Problem Tic-Tac-Toe
8 3 4
1 5 9
6 7 2
15 - (8 5)
29
Introductory Problem Tic-Tac-Toe
  • 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.

30
Introductory Problem Tic-Tac-Toe
  • 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.

31
Introductory Problem Tic-Tac-Toe
  • Comments
  • 1. Require much more time to consider all
    possible
  • moves.
  • 2. Could be extended to handle more complicated
  • games.

32
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
    with
  • human thinking, activities such as
    decision-making,
  • problem solving, learning ..."
  • (Bellman, 1978)

33
Exercises
  • "The study of mental faculties, through the use
    of
  • computational models"
  • (Charniak and McDermott, 1985)
  • "The study of the computations that make it
    possible to
  • perceive, reason, and act"
  • (Winston, 1992)
  • "The art of creating machines that perform
    functions that
  • require intelligence when performed by people"
  • (Kurzweil, 1990)

34
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
    emulate
  • intelligent behavior in terms of computationl
    processes"
  • (Schalkoff, 1990)
  • "The branch of computer science that is concerned
    with
  • the automation of intelligent behaviour"
  • (Luger and Stubblefield, 1993)

35
Exercises
  • "A collection of algorithms that are
    computationally
  • tractable, adequate approximations of
    intractabiliy
  • specified problems"
  • (Partridge, 1991)
  • "The enterprise of constructing a physical symbol
  • system that can reliably pass the Turing test"
  • (Ginsberge, 1993)
  • "The f ield of computer science that studies how
  • machines can be made to act intelligently"
  • (Jackson, 1986)
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