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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.       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

Learning resources

Textbook

Lecture Notes.

Journals

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

Reference books

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.

Grading

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.

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School of Engineering and technologies     Asian Institute of Technology