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Paradigms of Artificial Intelligence

Course code: AT70.10
Credits: 3(3–0)
This course is elective

Course objectives

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.

Learning outcome

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

Prerequisite(s)

Consent of the Instructor

Course outline

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.       Learning resources

Textbook

Lecture Notes.

Journals

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.

Reference books

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

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