CS6659 ARTIFICIAL INTELLIGENCE

General info:  CS6659 ARTIFICIAL INTELLIGENCE	University – Anna university, Tamil Nadu, India Marks: UNIT 1 to 5 – 9+3 each unit  Period - TOTAL (L:45+T:15): 60 PERIODS
OBJECTIVES: The student should be made to: 1)  Study the concepts of Artificial Intelligence. 2)  Learn the methods of solving problems using Artificial Intelligence. 3)  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 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 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 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  Expert systems - Architecture of expert systems, Roles of expert systems - Knowledge Acquisition – Meta knowledge, Heuristics. Typical expert systems - MYCIN, DART, XOON, Expert systems shells.
OUTCOMES: At the end of the course, the student should be able to: 1)  Identify problems that are amenable to solution by AI methods. 2)  Identify appropriate AI methods to solve a given problem. 3)  Formalise a given problem in the language/framework of different AI    methods. 4)   Implement basic AI algorithms. 5)  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)”, Mc Graw Hill- 2008.  (Units-I,II,VI & V) 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
Notes from studentfocus.com: Unit-1,Unit-2, Unit-3, Unit-4, Unit-5