Title: Artificial Intelligence
1Artificial Intelligence
2Outline
- Introduction
- Problems and Search
- Knowledge Representation
- Advanced Topics
- - Game Playing
- - Uncertainty and Imprecision
- - Planning
- - Machine Learning
3References
- Artificial Intelligence (1991)
- Elaine Rich Kevin Knight, Second Ed, Tata
McGraw Hill - Decision Support Systems and Intelligent Systems
- Turban and Aronson, Sixth Ed, PHI
4Introduction
- What is AI?
- The foundations of AI
- A brief history of AI
- The state of the art
- Introductory problems
5What is AI?
6What 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
7What is AI?
Thinking humanly Thinking rationally
Acting humanly Acting rationally
8Acting Humanly The Turing Test
- Alan Turing (1912-1954)
- Computing Machinery and Intelligence (1950)
Imitation Game
Human
Human Interrogator
AI System
9Acting 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.
10Thinking 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.
11Thinking 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.
12Acting 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.
13The 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.
14The Foundations of AI
- Psychology (1879 - present)
- - Adaptation.
- - Phenomena of perception and motor control.
- - Experimental techniques.
- Linguistics (1957 - present)
- - Knowledge representation.
- - Grammar.
-
15A 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. -
16A 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..
-
-
17A 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.
18Task 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
19AI 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
20The 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.
- ......
21Tic Tac Toe
- Three programs are presented
- Series increase
- Their complexity
- Use of generalization
- Clarity of their knowledge
- Extensability of their approach
22Introductory Problem Tic-Tac-Toe
X X
o
23Introductory 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.
24Introductory 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.
25Introductory Problem Tic-Tac-Toe
1 2 3
4 5 6
7 8 9
26Introductory 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))
- .......
27Introductory 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.
28Introductory Problem Tic-Tac-Toe
8 3 4
1 5 9
6 7 2
15 - (8 5)
29Introductory 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.
30Introductory 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.
31Introductory Problem Tic-Tac-Toe
- Comments
- 1. Require much more time to consider all
possible - moves.
- 2. Could be extended to handle more complicated
- games.
32Exercises
- 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)
33Exercises
- "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)
34Exercises
- "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)
35Exercises
- "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)