Section-A
Foundational issues in intelligent systems: Foundation and history of AI, Ai problems and techniques – AI programming
languages, introduction to LISP and PROLOG- problem spaces and searches, blind search strategies, Breadth first- Depth first- heuristic
search techniques Hill climbing: best first- A * algorithm AO* algorithm-game tree, Min max algorithms, game playing- alpha beta
pruning.
Section-B
Knowledge representation issues, predicate logic- logic programming, semantic nets- frames and inheritance, constraint propagation,
representing knowledge using rules, rules based deduction systems.
Reasoning under uncertainty, review of probability, Baye‟s probabilistic interferences and Dempster shafer theory, Heuristic methods,
Section-C
Symbolic reasoning under uncertainty, Statistical reasoning, Fuzzy reasoning, Temporal reasoning, Non monotonic reasoning.
Planning, planning in situational calculus, representation for planning, partial order planning algorithm,
Section-D
Learning from examples, discovery as learning, I earning by analogy, explanation based learning, neural nets, genetic algorithms.
Principles of Natural language processing, rule based systems architecture, Expert systems, knowledge acquisition concepts, AI
application to robotics, and current trends in intelligent systems.