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                                     

Introduction to AI-Problem formulation, Problem Definition -Production systems, Control strategies,sSearch strategies. Problem characteristics, Production system characteristics -Specialized productions 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.

                                                                                                                             

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)”, 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