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CS 621 Artificial Intelligence
Lecture 1 – 28/07/05
Prof. Pushpak Bhattacharyya
Introduction
28-07-05
Prof. Pushpak Bhattacharyya, IIT
Bombay
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AI Introduction
Language Processing
Expert
System
Planning
Search
Reasoning
Learning
Knowledge Representation
Vision
Robotics
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Thinking Ability in Machines
“Can machines think”
320 BC Aristotle – Syllogism (Disjunctive Reasoning)
ex: All men are mortal.
Shakespeare is a man.
Shakespeare is mortal.
Inductive Reasoning – specific  general (difficult for machine)
ex: Crows in Bhopal are black.
Crows in Mumbai are black.
All crows are black.
Abductive Learning - p  q
if Q is true then P is true in absence of any other info.
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Turing Machine
Finite state control
Infinite tape
Church-Turing Hypothesis
Anything that is computable, is computable by a TM & viceversa.
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Bombay
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Turing Test
Human
Computer
Interrogator has to find which is a
machine and which is a human by
asking questions to both of them. If the
machine is able to fool the interrogator
to behave like a human, then that
machine passes the Turing Test.
Interrogator
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Machines
Gene/DNA: A string of bits which is a coded instruction.
ANIMATE THEORY: Intelligence is producable only on carbon base.
Silicon intelligence is not possible.
PHYSICAL SYMBOL HYPOTHESIS: (opposed to Animate theory):
Intelligence emerges from manipulating symbols & nothing else is
needed.
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Levels in AI
Knowledge level (Disambiguation)
● Symbol level (e.g. Lisp program)
● Signal level (e.g. Neural Net)
●
AI
Science (Cognitive Sc, Psychology, social science, physics
Sci + Engg (theory + code)
Engineering
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Search
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Search is Fundamental and
Ubiquitous
Fundamental in all of AI.
In planning :
A
B
C
B
A
C
Table
At every stage: multiple possibilities, search for the best possible
sequence of options.
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Bombay
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In Vision
R
World
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Two eye system
Left eye retina & right eye retina have
pixels activated. Search for corresponding
points:
points in the left eye should correspond to
points in the right eye – O(n2) algorithm.
Prof. Pushpak Bhattacharyya, IIT
Bombay
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In Language Processing
The man would like to play.
Noun
Verb
Prep Verb Noun Verb
System has to search amongst the possibilities to get the correct meaning.
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Abstractions of Search
State
 state space
● Operators
 transform states
● Cost
 associated operations
● Start node
● Goal node (S)
● Minimal cost path
●
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Classical Example
8-puzzle
4
2
5
1
2
3
3
1
6
4
5
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7
7
8
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Goal
Start (S0)
State: Matrix repersentation of the board.
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Operators
Here operators are movements of blank space.
MU
4
3
8
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5
1
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MR
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ML
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Bombay
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Cost
Cost of each operation = 1 for this problem.
Which path leads to the goal –
Uninformed search (Breadth first, Depth first)
●
Heuristic Search (A* algorithm)
●
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Search
All searchs are instantiations of “General Graph Search” (GGS) algo.
Input:
Output:
The state space graph (implicit/explicit).
The path to the goal node.
Data Structures: 1. Open list (OL)
2. Closed list (CL)
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Example
Start
A
1
3
B
4
D
C
6
2
E
9
8
F
State space graph
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OL
CL
:A
:
OL
CL
:BCD
:A
OL
CL
:CDE
:AB
OL
CL
:DEF
:ABC
OL
CL
:EFG
:ABCD
G
Prof. Pushpak Bhattacharyya, IIT
Bombay
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KNOWLEDGE REPRESENTATION &
INFERENCING
USING PREDICATE CALCULUS
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Bombay
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Example
Example:
John, Jack & Jill are members of Alpine club. Every member if the club
is either a mountain climber or a skier. All skiers like snow. No
mountain climber likes rain. Jack dislikes whatever John likes, and likes
whatever John dislikes. John likes rain and snow.
Is there a member who is a mountain climber but not a skier.
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Knowledge Representation
1. member (John, Alpine)
2. member (Jack, Alpine)
3.member (Jill, Alpine)
 sk(x)]
4. x [member(x, Alpine) → mc(x)
5. x [sk(x) → like(x, snow)]
6. x [mc(x) → ~like(x, rain)]
7.  x [like(John, x) → ~like(John, x)]
8.  x [~like(John, x) → like(Jack, x)]
9. like(John, rain)
10. like(John, snow)


Ques: x [member(x, Alpine)
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mc(x)
Prof. Pushpak Bhattacharyya, IIT
Bombay
~sk(x)]
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Inference Strategy - RESOLUTION
Basic Idea: REFUTATION of the goal
Proof by contradiction
Suppose the goal is false. Then show contradiction in the knowledge base.
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Bombay
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Steps in Inferencing
Convert all expressions, including the falsified goal, into clauses.
1. member (John, Alpine)
2. member (Jack, Alpine)
3. member (Jill, Alpine)
4. ~ member(x1, Alpine) ν mc(x1) ν sk(x1)
5. ~ sk(x2) ν like(x2,snow)
6. ~ mc(x3) ν ~ like(x3,rain)
7. ~ like(John, x4) ν ~ like(Jack, x4)
8. like(John, x5) ν like(Jack, x5)
9. like(John, rain)
10. like(John, snow)
11. ~ member(x6, Alpine) ν ~ mc(x6) ν sk(x6)
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Run Resolution
By unification find value for x6
Theory of resolution: given P & ~P ν Q
Resolvents
we can obtain Q (Resolute)
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Inverted Tree Diagram
~P ν Q
P
Q
Aim
C1
C2
Indicates contradiction
null clause
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Goal with Negation

~[ x{(member (x, Alpine) Λ mc(x) Λ ~ sk(x))}]

x[~ member (x, Alpine) ν ~ mc(x) ν sk(x)]
11. ~ member(x6, Alpine) ν ~ mc(x6) ν
sk(x6)
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Monotonic Logic
Every step of resolution increases the knowledge base monotonically.
Non-monotonic logic which used default reasoning.
NEGATION BY FAILURE
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Modus Ponens & Modus Tolens
Modus Ponen:
P & P →Q
gives Q
Modus Tolens:
~Q and P →Q
gives ~P
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Prolog
Predicate calculus (HORN Clause)
+
Resolution
HORN Clauses: All the implications have a single literal as consequent.
A(antecedent) → B(consequent)
B is a single literal, never contain any operator.
Moreover B has to be a positive literal.
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Bombay
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Summary
• AI is a fascinating discipline, needing input from many
branches of knowledge.
• Scaling up and robustness are the needs of today’s world.
• Web has introduced new challenges to the field.
• Language processing and machine learning have assumed
great importance.
• In this lecture we took a look at two core areas: search and
reasoning, which will be developed further.
• Will delve into other areas as the course progresses.
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Prof. Pushpak Bhattacharyya, IIT
Bombay
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Reading Material
• Basic Text Books
– Russell S, Norvig P (1995) Artificial Intelligence: A
Modern Approach, Prentice Hall Series in Artificial
Intelligence. Englewood Cliffs, New Jersey
– Nilsson N. J. (1980) Principles of Artificial Intelligence,
Morgan Kaufmann Publishers Inc.
• Journals
– AI, IEEE SMC, Machine Learning, Computational
Linguistics
• Proceedings of
– AAAI, ECAI, IJCAI, ICML, ACL, COLING etc. etc.
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Bombay
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