CS6659 ARTIFICIAL INTELLIGENCE syllabus-subject-notes-pevious-year-questions-papers-bank
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.
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.
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.
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.
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