Semester 7>AI

1. INTRODUCTION TO AI AND SEARCH TECHNIQUES:
Foundation and history of AI; data, information and knowledge; AI problems and techniques – AI programming languages, problem space representation with examples; blind search strategies, breadth first search, depth first search, heuristic search techniques: hill climbing: best first search, A * algorithm AO* algorithm, Means-ends analysis.

2. KNOWLEDGE REPRESENTATION ISSUES:
predicate logic; logic programming; constraint propagation; representing knowledge using rules.

3. REASONING UNDER UNCERTAINITY:
Reasoning under uncertainty, non monotonic reasoning; Review of probability; Bayes‘probabilistic interferences and Dempster Shafer theory; heuristic methods; symbolic reasoning under uncertainty; statistical reasoning, fuzzy reasoning.

4. PLANNING & GAME PLAYING:
Minimax search procedure; goal stack planning; non linear planning, hierarchical planning, planning insituational calculus; representation for planning; partial order planning algorithm.

5. LEARNING:
concepts; rote learning, learning by taking advices, learning by problem solving, learning from examples, discovery as learning, learning by analogy; explanation based learning; neural nets; genetic algorithms.

6. OTHER KNOWLEDGE STRUCTURES:
semantic nets, partitioned nets, parallel implementation of semantic nets; frames, common sense reasoning and thematic role frames; architecture of knowledge based system; rule based systems; forward and backward chaining; frame based systems.

7. APPLICATIONS OF ARTIFICIAL INTELLIGENCE:
Principles of natural language processing; rule based systems architecture; expert systems, knowledge acquisition concepts; AI application to robotics, and current trends in intelligent systems; parallel and distributed AI: psychological modeling, parallelism in reasoning systems, distributed reasoning systems and algorithms

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