OBJECTIVE: To study the concepts of Artificial Intelligence and methods of solving problems using Artificial Intelligence and introduce the concepts of Expert Systems and machine learning.
1. INTRODUCTION TO Al AND PRODUCTION SYSTEMS 
Introduction to Al — Problem formulation, Problem Definition — Production systems, Control strategies, Search strategies. Problem characteristics, Production system characteristics — Specialized production systems — Problem solving methods — Problem graphs, Matching, Indexing and Heuristic functions — Hill Climbing, Depth first and Breath first, Constraints satisfaction — Related algorithms, Measure of performance and analysis of search algorithms.
2. REPRESENTATION OF KNOWLEDGE 
Game playing — Knowledge representation, Knowledge representation using Predicate logic, Introduction to predicate calculus, Resolution, Use of predicate calculus, Knowledge representation using other logic — Structured representation of knowledge.
3. FUNDAMENTALS OF EXPERT SYSTEMS 
Basic plan generation systems — Strips — Advanced plan generation systems — K strips — D Comp. Expert systems — Architecture of expert systems, Roles of expert systems — Knowledge Acquisition — Meta knowledge, Heuristics.
4. KNOWLEDGE INFERENCE 
Knowledge representation — Production based system, Frame based system. Inference — Backward chaining, Forward chaining, Rule value approach, Fuzzy reasoning — Certainty factors, Bayesian probability.
5. MACHINE LEARNING 
Strategic explanations — Why, Why not and how explanations. Learning — Machine learning, adaptive learning.
Typical expert systems — MYCIN, PIP, INTERNIST, DART, XOON, Expert systems shells
1. Elaine Rich, “Artificial Intelligence”, 1985, McGraw Hill.
2. Nilsson N.J., “Principles of Artificial Intelligence”, 1992, Narosa.
1. Hayes & Roth, “Building Expert Systems”, 1982, Narosa.