RGTU/RGPV MCA-401 Artificial Intelligence & Applications Syllabus MCA 4th -Fourth sem Syllabus

RGTU/RGPV MCA-401 Artificial Intelligence & Applications Syllabus
MCA 4th Sem MCA-401 Artificial Intelligence & Applications Syllabus
MCA 4th - Fourth Semester Syllabus

MCA-401 Artificial Intelligence & Applications Course Contents:

UNIT-I
General Issues and Overview of AI
The AI problems, what is an AI technique, Characteristics of AI applications. Introduction to LISP programming: Syntax and numeric functions, Basic list manipulation functions, predicates and conditionals, input output and local variables, iteraction and recursion, property lists and arrays.

UNIT-II
Problem Solving, Search and Control Strategies
General problem solving, production systems, control strategies forward and backward chaining, exhausive searches depth first breadth first search. 
Heuristic Search Techniques Hill climbing, branch and bound technique, best first search & A* algorithm, AND / OR graphs, problem reduction & AO* algorithm, constraint satisfaction problems.

UNIT-III
Knowledge Representations

First order predicate calculus, skolemization, resolution principle & unification, interface mechanisms,
horn's clauses, semantic networks, frame systems and value inheritance, scripts, conceptual dependency.

UNIT-IV
Natural Language processing
Parsing techniques, context free grammer, recursive transitions nets (RNT), augmented transition nets (ATN), case and logic grammers, symantic analysis. Game playing Minimax search procedure, alpha-beta cutoffs, additional refinments. 
Planning Overview an example domain the block word, component of planning systems, goal stack planning, non linear planning.

UNIT-V
Probabilistic Reasoning and Uncertainty
Probability theory, bayes theorem and bayesian networks, certainty factor. 
Expert Systems Introduction to expert system and application of expert systems, various expert system shells, vidwan frame work, knowledge acquisition, case studies, MYCIN. Learning Rote learning, learning by induction, explanation based learning.

BOOKS
1. Elaine Rich and Kevin Knight “Artifical Intelligence” - Tata McGraw Hill.
2. “Artifical Intelligence” 4 ed. Pearson.
3. Dan W. Patterson “Introduction to Artifical Intelligence and Expert Systems”, Prentice India.
4. Nils J. Nilson “Principles of Artifical Intelligence”, Narosa Publishing House.
5. Clocksin & C.S.Melish “Programming in PROLOG”, Narosa Publishing House.
6. M.Sasikumar,S.Ramani etc. “Rule based Expert System”, Narosa Publishing House.

Note : Paper is to be set unit wise with internal choice.

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