CS6659 Artificial Intelligence 6th Sem Question Paper ND18 Regulation 2013

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Anna University
Question Paper Code : 20339
B.E/B.Tech. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2018.
Sixth Semester
Computer Science and Engineering
CS6659 Artificial Intelligence
( Common to Electronics and Instrumentation Engineering / Instrumentation and Control Engineering / Information Technology )
( Regulation 2013 )

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CS6659 Artificial Intelligence Syllabus

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
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

OUTCOMES:
At the end of the course, the student should be able to:
-Identify problems that are amenable to solution by AI methods.
-Identify appropriate AI methods to solve a given problem.
-Formalise a given problem in the language/framework of different AI methods.
-Implement basic AI algorithms.
-Design and carry out an empirical evaluation of different algorithms on a problemformalisation, and state the conclusions that the evaluation supports.

TEXT BOOKS:
1. Kevin Night and Elaine Rich, Nair B., “Artificial Intelligence (SIE)”, Mc Graw Hill- 2008. (Units-I,II,VI & V)
2. Dan W. Patterson, “Introduction to AI and ES”, Pearson Education, 2007. (Unit-III).

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