1. CS 621 Artificial Intelligence Lecture 1 – 28/07/05
Prof. Pushpak Bhattacharyya
Introduction
Prof. Pushpak Bhattacharyya, IIT Bombay

2. AI Introduction Language Processing Expert Search System Reasoning
Learning
Knowledge Representation
Vision
Planning
Robotics
Prof. Pushpak Bhattacharyya, IIT Bombay

3. 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.
Prof. Pushpak Bhattacharyya, IIT Bombay

4. Prof. Pushpak Bhattacharyya, IIT Bombay
Turing Machine
Finite state control
Infinite tape
Church-Turing Hypothesis
Anything that is computable, is computable by a TM & viceversa.
Prof. Pushpak Bhattacharyya, IIT Bombay

5. Prof. Pushpak Bhattacharyya, IIT Bombay
Turing Test
Computer
Human
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
Prof. Pushpak Bhattacharyya, IIT Bombay

6. Prof. Pushpak Bhattacharyya, IIT Bombay
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.
Prof. Pushpak Bhattacharyya, IIT Bombay

7. Prof. Pushpak Bhattacharyya, IIT Bombay
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
Prof. Pushpak Bhattacharyya, IIT Bombay

8. Prof. Pushpak Bhattacharyya, IIT Bombay
Search
Prof. Pushpak Bhattacharyya, IIT Bombay

9. Search is Fundamental and Ubiquitous
Fundamental in all of AI.
In planning : C B A B C A
Table
At every stage: multiple possibilities, search for the best possible sequence of options.
Prof. Pushpak Bhattacharyya, IIT Bombay

10. Prof. Pushpak Bhattacharyya, IIT Bombay
In Vision R L
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.
World
Prof. Pushpak Bhattacharyya, IIT Bombay

11. 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.
Prof. Pushpak Bhattacharyya, IIT Bombay

12. Abstractions of Search
State  state space
Operators  transform states
Cost  associated operations
Start node
Goal node (S)
Minimal cost path
Prof. Pushpak Bhattacharyya, IIT Bombay

13. Prof. Pushpak Bhattacharyya, IIT Bombay
Classical Example
8-puzzle 4 2 5 1 2 3 6 6 3 1 4 5 8 7 7 8
Start (S0)
State: Matrix repersentation of the board.
Goal
Prof. Pushpak Bhattacharyya, IIT Bombay

14. Prof. Pushpak Bhattacharyya, IIT Bombay
Operators
Here operators are movements of blank space. 4 2 5 3 1 6 MU 8 7 4 2 5 MR 4 2 5 6 3 1 3 1 6 8 7 8 7 ML 4 2 5 3 1 6 8 7
Prof. Pushpak Bhattacharyya, IIT Bombay

15. Prof. Pushpak Bhattacharyya, IIT Bombay
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)
Prof. Pushpak Bhattacharyya, IIT Bombay

16. Prof. Pushpak Bhattacharyya, IIT Bombay
Search
All searchs are instantiations of “General Graph Search” (GGS) algo.
Input: The state space graph (implicit/explicit).
Output: The path to the goal node.
Data Structures: 1. Open list (OL)
2. Closed list (CL)
Prof. Pushpak Bhattacharyya, IIT Bombay

17. Prof. Pushpak Bhattacharyya, IIT Bombay
Example
OL :A
CL :
OL : B C D
CL : A
OL : C D E
CL : A B
OL : D E F
CL : A B C
OL : E F G
CL : A B C D A
Start 1 3 4 B C D 6 2 9 8 3 E F G
State space graph
Prof. Pushpak Bhattacharyya, IIT Bombay

18. KNOWLEDGE REPRESENTATION & INFERENCING USING PREDICATE CALCULUS
Prof. Pushpak Bhattacharyya, IIT Bombay

19. Prof. Pushpak Bhattacharyya, IIT Bombay
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.
Prof. Pushpak Bhattacharyya, IIT Bombay

20. Knowledge Representation
member (John, Alpine)
member (Jack, Alpine)
member (Jill, Alpine)
x [member(x, Alpine) → mc(x) sk(x)]
x [sk(x) → like(x, snow)]
x [mc(x) → ~like(x, rain)]
x [like(John, x) → ~like(John, x)]
x [~like(John, x) → like(Jack, x)]
like(John, rain)
like(John, snow)
Ques: x [member(x, Alpine) mc(x) ~sk(x)]
Prof. Pushpak Bhattacharyya, IIT Bombay

21. Inference Strategy - RESOLUTION
Basic Idea: REFUTATION of the goal
Proof by contradiction
Suppose the goal is false. Then show contradiction in the knowledge base.
Prof. Pushpak Bhattacharyya, IIT Bombay

22. Prof. Pushpak Bhattacharyya, IIT Bombay
Steps in Inferencing
Convert all expressions, including the falsified goal, into clauses.
member (John, Alpine)
member (Jack, Alpine)
member (Jill, Alpine)
~ member(x1, Alpine) ν mc(x1) ν sk(x1)
~ sk(x2) ν like(x2,snow)
~ mc(x3) ν ~ like(x3,rain)
~ like(John, x4) ν ~ like(Jack, x4)
like(John, x5) ν like(Jack, x5)
like(John, rain)
like(John, snow)
~ member(x6, Alpine) ν ~ mc(x6) ν sk(x6)
Prof. Pushpak Bhattacharyya, IIT Bombay

23. Prof. Pushpak Bhattacharyya, IIT Bombay
Run Resolution
By unification find value for x6
Theory of resolution: given P & ~P ν Q
Resolvents
we can obtain Q (Resolute)
Prof. Pushpak Bhattacharyya, IIT Bombay

24. Prof. Pushpak Bhattacharyya, IIT Bombay
Inverted Tree Diagram P
~P ν Q Q
Aim C1 C2
Indicates contradiction
null clause
Prof. Pushpak Bhattacharyya, IIT Bombay

25. Prof. Pushpak Bhattacharyya, IIT Bombay
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)
Prof. Pushpak Bhattacharyya, IIT Bombay

26. Prof. Pushpak Bhattacharyya, IIT Bombay
Monotonic Logic
Every step of resolution increases the knowledge base monotonically.
Non-monotonic logic which used default reasoning.
NEGATION BY FAILURE
Prof. Pushpak Bhattacharyya, IIT Bombay

27. Modus Ponens & Modus Tolens
P & P →Q
gives Q
Modus Tolens:
~Q and P →Q
gives ~P
Prof. Pushpak Bhattacharyya, IIT Bombay

28. Prof. Pushpak Bhattacharyya, IIT Bombay
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.
Prof. Pushpak Bhattacharyya, IIT Bombay

29. Prof. Pushpak Bhattacharyya, IIT Bombay
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.
Prof. Pushpak Bhattacharyya, IIT Bombay

30. Prof. Pushpak Bhattacharyya, IIT Bombay
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.
Prof. Pushpak Bhattacharyya, IIT Bombay