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)