Vishwakarma Institute of Technology Master of Computer ...
Vishwakarma Institute of Technology Master of Computer ... Vishwakarma Institute of Technology Master of Computer ...
BRACT’S Vishwakarma Institute of Technology, Pune – 411 037 Department of Computer Engineering Structure & Syllabus of MCA Pattern C11, issue 03, Rev 01 Dt 2/4/2011 24 FF No. : 654 Credits: 03 Teaching Scheme: - Theory 3 Hrs/Week Prerequisites: Nil CS72111::ARTIFICIAL INTELLIGENCE Objectives: • To provide a strong foundation of fundamental concepts in Artificial Intelligence • To provide a basic exposition to the goals and methods of Artificial Intelligence • To enable the student to apply these techniques in applications which involve perception, reasoning and learning. • To learn and understand the knowledge representation techniques for knowledge base Unit I (9+2 Hrs) Introduction A. Definition, what is A.I? Foundation of A.I., History, A.I. Representation, AI Techniques, Importance of AI, Representation of Knowledge, Knowledge Base Systems, State Space Search, Production Systems, Problem Characteristics. B. Criteria for Success, Turing Test. Unit II (9+2 Hrs) Heuristics Search Techniques A. Depth First Search, Breadth First Search, Generate & test, Hill Climbing, Best First Search, A* and AO* Algorithm, Constraint satisfaction, Means-Ends Analysis. Game playing: Minimax Search, Alpha-Beta Cutoffs, Waiting for Quiescence, Secondary search. B. Applications Minimax Algorithm.
BRACT’S Vishwakarma Institute of Technology, Pune – 411 037 Department of Computer Engineering Unit III (9+2 Hrs) Predicate Logic A. Using predicate logic: Predicate Calculus, Predicate and arguments, ISA Hierarchy, Frame notation, Resolution, Natural Deduction. Representing simple facts in Logic - Logic Programming, Computable functions in predicates, resolution, unification, Forward and backward reasoning, Expert System, Basics of PROLOG. B. Propositional Logic, Case study of Expert system in PROLOG. Unit IV (9+2 Hrs) Structured Knowledge Representation A. TMS (Truth maintenance system),Statistical and probabilistic reasoning ,Associative Networks, Semantic Nets, Frames Structures, Learning – Concept of Learning – Learning Automata, Genetic Algorithm, Learning by induction Planning: Block world, strips, Implementation using goal stack, Non linear planning, Hierarchical planning, and least commitment strategy. B. Conceptual Dependency & Scripts, Various types of learning. Unit V (9+2 Hrs) Natural Language Processing A. Introduction, Syntactic processing, Semantic analysis and representation structures. Grammers and Parsers, Discourse and Pragmatic processing. B. Augmented Transition Network, Understanding. Text Books 1. "Artificial Intelligence", Eiaine Rich and Kerin Knight,Tata McGraw Hill,ISBN 0-07- 460081-8. 2. "Artificial Intelligence : A Modern Approach", Stuart Russell & Peter Nerving Pearson Education, 2 nd Edition. Reference Books 1. "Prolog Programming For Artificial Intelligence" , Ivan Bratko , 2 nd Edition Addison Wesley, 1990. 2. "Introduction to Artificial Intelligence.", Eugene, Charniak, Drew Mcdermott, Addison Wesley Structure & Syllabus of MCA Pattern C11, issue 03, Rev 01 Dt 2/4/2011 25
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BRACT’S<br />
<strong>Vishwakarma</strong> <strong>Institute</strong> <strong>of</strong> <strong>Technology</strong>, Pune – 411 037<br />
Department <strong>of</strong> <strong>Computer</strong> Engineering<br />
Unit III (9+2 Hrs)<br />
Predicate Logic<br />
A. Using predicate logic: Predicate Calculus, Predicate and arguments, ISA Hierarchy,<br />
Frame notation, Resolution, Natural Deduction. Representing simple facts in Logic -<br />
Logic Programming, Computable functions in predicates, resolution, unification, Forward<br />
and backward reasoning, Expert System, Basics <strong>of</strong> PROLOG.<br />
B. Propositional Logic, Case study <strong>of</strong> Expert system in PROLOG.<br />
Unit IV (9+2 Hrs)<br />
Structured Knowledge Representation<br />
A. TMS (Truth maintenance system),Statistical and probabilistic reasoning ,Associative<br />
Networks, Semantic Nets, Frames Structures, Learning – Concept <strong>of</strong> Learning – Learning<br />
Automata, Genetic Algorithm, Learning by induction Planning: Block world, strips,<br />
Implementation using goal stack, Non linear planning, Hierarchical planning, and least<br />
commitment strategy.<br />
B. Conceptual Dependency & Scripts, Various types <strong>of</strong> learning.<br />
Unit V (9+2 Hrs)<br />
Natural Language Processing<br />
A. Introduction, Syntactic processing, Semantic analysis and representation structures.<br />
Grammers and Parsers, Discourse and Pragmatic processing.<br />
B. Augmented Transition Network, Understanding.<br />
Text Books<br />
1. "Artificial Intelligence", Eiaine Rich and Kerin Knight,Tata McGraw Hill,ISBN 0-07-<br />
460081-8.<br />
2. "Artificial Intelligence : A Modern Approach", Stuart Russell & Peter Nerving<br />
Pearson Education, 2 nd Edition.<br />
Reference Books<br />
1. "Prolog Programming For Artificial Intelligence" , Ivan Bratko , 2 nd Edition Addison<br />
Wesley, 1990.<br />
2. "Introduction to Artificial Intelligence.", Eugene, Charniak, Drew Mcdermott,<br />
Addison Wesley<br />
Structure & Syllabus <strong>of</strong> MCA Pattern C11, issue 03, Rev 01 Dt 2/4/2011<br />
25
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