CS6659 ARTIFICIAL INTELLIGENCE | Syllabus

CS6659   ARTIFICIAL   INTELLIGENCE   L T P C   3 0 0 3 

OBJECTIVES:
 The student should be made to:

•  Study the concepts of Artificial Intelligence.
•  Learn the methods of solving problems using Artificial Intelligence.
•  Introduce the concepts of Expert Systems and machine learning.

UNIT I                 INTRODUCTION TO Al AND PRODUCTION SYSTEMS                         (9)
 Introduction to AI-Problem formulation, Problem Definition -Production systems, Control strategies, Search strategies. Problem characteristics, Production system characteristics -Specialized production system- 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.

UNIT II                         REPRESENTATION OF KNOWLEDGE                                              (9)
 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.

UNIT III                          KNOWLEDGE INFERENCE                                                               (9) 
Knowledge representation -Production based system, Frame based system. Inference - Backward chaining, Forward chaining, Rule value approach, Fuzzy reasoning - Certainty factors, Bayesian Theory-Bayesian Network-Dempster - Shafer theory

. UNIT IV                          PLANNING AND MACHINE LEARNING                                       (9) 
Basic plan generation systems - Strips -Advanced plan generation systems – K strips -Strategic explanations -Why, Why not and how explanations. Learning- Machine learning, adaptive Learning.

UNIT V                               EXPERT SYSTEMS                                                                              (9) 
Expert systems - Architecture of expert systems, Roles of expert systems - Knowledge Acquisition –Meta knowledge, Heuristics. Typical expert systems - MYCIN, DART, XOON, Expert systems shells.

                                                                                                         TOTAL: 45 PERIODS

TEXT BOOKS:
1. Kevin Night and Elaine Rich, Nair B., “Artificial Intelligence (SIE)”, McGraw Hill- 2008. (Unit-1,2,4,5).
2. Dan W. Patterson, “Introduction to AI and ES”, Pearson Education, 2007. (Unit-III)

REFERENCES:
1. Peter Jackson, “Introduction to Expert Systems”, 3rd Edition, Pearson Education, 2007.
2. Stuart Russel and Peter Norvig “AI – A Modern Approach”, 2nd Edition, Pearson Education 2007.
3. Deepak Khemani “Artificial Intelligence”, Tata Mc Graw Hill Education 2013.
4. http://nptel.ac.in/

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