CS6659 AI Question Papers, Artificial Intelligence Previous Year Question Papers – IT 6th SEM Anna University

Anna University Regulation 2013 Information Technology (IT) CS6659 AI old Question Papers for previous years are provided below. Download link for IT 6th SEM CS6659 Artificial Intelligence Previous Year Question Papers are listed down for students to make perfect utilization and score maximum marks with our study materials.

Anna University Regulation 2013 Information Technology (IT) 6th SEM CS6659 AI -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
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 problem formalisation, and state the conclusions that the evaluation supports.

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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