CS621: Artificial Intelligence

Pushpak BhattacharyyaCSE Dept., IIT Bombay

Lecture–1: Introduction

Persons involved Faculty instructor: Dr. Pushpak

Bhattacharyya (www.cse.iitb.ac.in/~pb) TAs: Saurabh (saurabhsohoney@cse),

Anup (anup@cse) Course home page www.cse.iitb.ac.in/~cs621-2009 Venue: CSE Building: S9 1 hour lectures 3 times a week: Mon-9.30,

Tue-10.30, Thu-11.30

Perspective

Disciplines which form the core of AI- inner circle Fields which draw from these disciplines- outer circle.

Planning

ComputerVision

NLP

ExpertSystems

Robotics

Search, Reasoning,Learning

2020 And Beyond ……..

Praful: “I have a meeting

with my boss and I am late

…….”

Phone: “Your friend had an emergency she is admitted at some hospital in

powai …”

Praful: “I should inform my agent to reschedule meeting”

Prafful’s Agent Negotiates WithBoss’s Agent and re-schedule meeting to tomorrow.

Agent: “Your meeting is re-scheduled to

tomorrow 5:00 PM”

Praful: “I still don’t know

where is he is admitted in powai …. I should use my agent ….”

Middle Agent

Praful’s Agent ContactsA Middle Agent to find out some hospital in powaihaving a recently admittedpatient named Vishal.

Agent: “Your friend is admitted

at New Powai Hospital Ward No.

9”

New Powai Hospital

Courtsey: this slides from the presentation on Semantic Web by Anup, Sapan, Nilesh, Tanmay

Motivation of Semantic Web (an IR and AI application)

Original driver: Automation - Make information on the Web more “machine-friendly” - Origins of the Semantic Web are in web metadata

Short term goal: Interoperability- Combining information from multiple sources- Web Services: discovery, composition

Long term goal: “Departure from the Tool Paradigm”- instead of using computers like tools, make them work on our behalf- removing humans from the loop to the extent possible

1.1 Semantic Web Architecture

Knowledge Representation

Knowledge Sharing

Reasoning

Trustworthiness

Courtsey: this slide from the presentation on Semantic Web by Anup, Sapan, Nilesh, Tanmay

1.2 Tree of Knowledge Technologies

AI Knowledge Representati

on

Semantic Technolog

y Languages

Content Management Languages

Process Knowledge LanguagesSoftware Modeling Languages

Courtsey: this slide from the presentation on Semantic Web by Anup, Sapan, Nilesh, Tanmay

Topics to be covered (1/2)

Search General Graph Search, A* Iterative Deepening, α-β pruning, probabilistic

methods Logic

Formal System Propositional Calculus, Predicate Calculus

Knowledge Representation Predicate calculus, Semantic Net, Frame Script, Conceptual Dependency, Uncertainty

Topics to be covered (2/2) Neural Networks: Perceptrons, Back Propagation, Self

Organization IR and AI Semantic Web and Agents Statistical Methods

Markov Processes and Random Fields Computer Vision, NLP, Machine Learning

Planning: Robotic Systems Confluence of NLP and CV: text and image based search Anthropomorphic Computing: Computational Humour,

Computational Music

Resources Main Text:

Artificial Intelligence: A Modern Approach by Russell & Norvik, Pearson, 2003.

Other Main References: Principles of AI - Nilsson AI - Rich & Knight Knowledge Based Systems – Mark Stefik

Journals AI, AI Magazine, IEEE Expert, Area Specific Journals e.g, Computational Linguistics

Conferences IJCAI, AAAI

Foundational Points

Church Turing Hypothesis Anything that is computable is

computable by a Turing Machine Conversely, the set of functions

computed by a Turing Machine is the set of ALL and ONLY computable functions

Turing MachineFinite State Head (CPU)

Infinite Tape (Memory)

Foundational Points (contd)

Physical Symbol System Hypothesis (Newel and Simon) For Intelligence to emerge it is

enough to manipulate symbols

Foundational Points (contd)

Society of Mind (Marvin Minsky) Intelligence emerges from the

interaction of very simple information processing units

Whole is larger than the sum of parts!

Foundational Points (contd)

Limits to computability Halting problem: It is impossible to

construct a Universal Turing Machine that given any given pair <M, I> of Turing Machine M and input I, will decide if M halts on I

What this has to do with intelligent computation? Think!

Foundational Points (contd)

Limits to Automation Godel Theorem: A “sufficiently

powerful” formal system cannot be BOTH complete and consistent

“Sufficiently powerful”: at least as powerful as to be able to capture Peano’s Arithmetic

Sets limits to automation of reasoning

Foundational Points (contd)

Limits in terms of time and Space NP-complete and NP-hard problems:

Time for computation becomes extremely large as the length of input increases

PSPACE complete: Space requirement becomes extremely large

Sets limits in terms of resources

Two broad divisions of Theoretical CS

Theory A Algorithms and Complexity

Theory B Formal Systems and Logic

AI as the forcing function Time sharing system in OS

Machine giving the illusion of attending simultaneously with several people

Compilers Raising the level of the machine for

better man machine interface Arose from Natural Language

Processing (NLP) NLP in turn called the forcing function for

AI

Search

Search is present everywhere

Planning (a) which block to pick, (b) which to stack, (c) which to

unstack, (d) whether to stack a block or (e) whether to unstack an already stacked block. These options have to be searched in order to arrive at the right sequence of actions.

A CB ABC

Table

Vision

A search needs to be carried out to find which point in the image of L corresponds to which point in R. Naively carried out, this can become an O(n2) process where n is the number of points in the retinal images.

World

Two eye system

R L

Robot Path Planning searching amongst the options of moving Left, Right, Up or

Down. Additionally, each movement has an associated cost representing the relative difficulty of each movement. The search then will have to find the optimal, i.e., the least cost path.

O1

R

D

O2

Robot Path

Natural Language Processing search among many combinations of parts of speech on the

way to deciphering the meaning. This applies to every level of processing- syntax, semantics, pragmatics and discourse.

The man would like to play.

Noun VerbNounVerb VerbPrepositio

n

Expert SystemsSearch among rules, many of which can apply to a

situation:

If-conditionsthe infection is primary-bacteremia AND the site of the culture is one of the sterile sites AND the suspected portal of entry is the gastrointestinal tract

THEN there is suggestive evidence (0.7) that infection is

bacteroid

(from MYCIN)

Algorithmics of Search

General Graph search Algorithm

S

A CB

F

ED

G

1 10

3

5 4 6

23

7

Graph G = (V,E)

1) Open List : S (Ø, 0)

Closed list : Ø

2) OL : A(S,1), B(S,3), C(S,10)

CL : S

3) OL : B(S,3), C(S,10), D(A,6)

CL : S, A

4) OL : C(S,10), D(A,6), E(B,7)

CL: S, A, B

5) OL : D(A,6), E(B,7)

CL : S, A, B , C

6) OL : E(B,7), F(D,8), G(D, 9)

CL : S, A, B, C, D

7) OL : F(D,8), G(D,9)

CL : S, A, B, C, D, E

8) OL : G(D,9)

CL : S, A, B, C, D, E, F

9) OL : Ø CL : S, A, B, C, D, E,

F, G

Algorithm A* One of the most important advances in AI

g(n) = least cost path to n from S found so far

h(n) <= h*(n) where h*(n) is the actual cost of

optimal path to G(node to be found) from n

S

n

G

g(n)

h(n)

“Optimism leads to optimality”

Search building blocks

State Space : Graph of states (Express constraints and parameters of the problem) Operators : Transformations applied to the states. Start state : S

0 (Search starts from here)

Goal state : {G} - Search terminates here. Cost : Effort involved in using an operator. Optimal path : Least cost path

ExamplesProblem 1 : 8 – puzzle

8

4

6

5

1

7

2

1

4

7

63 3

5

8

S

0

2

G

Tile movement represented as the movement of the blank space.Operators:L : Blank moves leftR : Blank moves rightU : Blank moves upD : Blank moves down

C(L) = C(R) = C(U) = C(D) = 1

Problem 2: Missionaries and Cannibals

Constraints The boat can carry at most 2 people On no bank should the cannibals outnumber the missionaries

River

R

L

Missionaries Cannibals

boat

boat

Missionaries Cannibals

State : <#M, #C, P>#M = Number of missionaries on bank L#C = Number of cannibals on bank LP = Position of the boat

S0 = <3, 3, L>G = < 0, 0, R >

OperationsM2 = Two missionaries take boatM1 = One missionary takes boatC2 = Two cannibals take boatC1 = One cannibal takes boatMC = One missionary and one cannibal takes boat

<3,3,L>

<3,1,R>

<2,2,R>

<3,3,L>

C2 MC

Partial search tree

Problem 3

B B W W WB

G: States where no B is to the left of any WOperators:1) A tile jumps over another tile into a blank tile with cost 22) A tile translates into a blank space with cost 1

All the three problems mentioned above are to be solved using A*

Allied Disciplines

Philosophy Knowledge Rep., Logic, Foundation of AI (is AI possible?)

Maths Search, Analysis of search algos, logic

Economics Expert Systems, Decision Theory, Principles of Rational Behavior

Psychology Behavioristic insights into AI programs

Brain Science Learning, Neural Nets

Physics Learning, Information Theory & AI, Entropy, Robotics

Computer Sc. & Engg. Systems for AI

Evaluation (i) Exams

Midsem Endsem Class test

(ii) Study Seminar

(iii) Work Assignments