CS6659
ARTIFICIAL INTELLIGENCE
L T P C
3 0 0 3
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.
· 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.
· 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).
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
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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