Semester Offering: August

This course provides a comprehensive exposure to the paradigms and techniques necessary for study and research in artificial intelligence. Emphasis is placed on the historical evolution and the emerging trends in technology.


Problem Representation. Search. Knowledge Representation and Reasoning. AI Programming. Learning. Knowledge Discovery. Negotiation. Turing Test and the Ontology of Intelligence. Cellular Automata. Neural Networks.


Consent of the Instructor


I.           Introduction
1.   Definition of AI, Historical Development of AI
2.    Applications of AI
3.    AI Techniques

II.         Problem Representation
1.       State-Space Representation
2.       Problem-Reduction Representation

III.       Search
1.       Blind and Non-Blind Searches
2.       Heuristic Search
3.       Best-First Search
4.       Optimal Search

IV.       Knowledge Representation and Reasoning
1.       Predicate Calculus
2.       Frame Representation  
3.       Semantic Networks
4.       Ontology of Knowledge Representation
5.       Fuzzy Representation

V.         AI Programming
1.       Lisp
2.       Prolog
3.       Web-Programming
VI.       Neural Networks
1.       Back Propagation
2.       Self-Organization
3.       Applications
VII.    Knowledge Discovery, Distributed Intelligence and Agents

VIII.   Turing Test and the Ontology of Intelligence

IX.       Cellular Automata
1.       Qualitative treatment of reproducing automata
2.       Artificial life
3.       Application

X.         Learning
1.       Symbolic learning models
2.       Connectionist learning models


Lecture Notes.


S.J. Russell and P. Norvig (1995):
Artificial Intelligence, A Modern Approach, Prentice Hall

N.J. Nilsson (1998):
Artificial Intelligence, A New Synthesis, Morgan Kaufmann

S. Wolfram (2002):
A New Kind of Science, Wolfram Media, Inc.


AI Magazine
Artificial Intelligence
IEEE Transactions on System, Man and Cybernetics
International Journal of Intelligent Systems
International Journal of Man-Machine Studies


The final grade will be computed from the following constituent parts:

Mid-semester exam (40%),
Final exam (40%) and
Assignments/projects (20%). 

Closed-book examination is given for both mid-semester and final exam.