Introduction, definition, underlying assumption, importance of AI & AI related fields.
Defining a problem. Production systems and its characteristics, Search and Control strategies – Generate and Test, Hill Climbing, Best – first Search, Problem reduction, Constraint Satisfaction, Means – Ends Analysis.
Representations and Mappings, Types of knowledge – Procedural Vs Declarative, Logic programming. Forward Vs Backward reasoning, Matching.
Representing simple facts, Instance and Isa relationships, Syntax and Semantics for Prepositional logic, FQPL and properties of Wffs, Conversion to Clausal form, Resolution, Natural deduction.
Symbolic reasoning under uncertainty, Probability and Bayes’ theorem, Certainity factors and Rule based systems, Bayesian Networks, Shafer Theory, Fuzzy Logic.
Structure and uses, Representing and using domain knowledge, Expert System Shells. Pattern recognition Learning classification patterns, recognizing and understanding speech. Introduction to knowledge Acquisition, Types of Learning.
MYCIN, Variants of MYCIN, PROSPECTOR, DENDRAL, PUFF, ETC.
Perceptrons, Checker Playing Examples, Learning Automata, Genetic Algorithms, Intelligent Editors.