AD 304 Artificial Intelligence

Syllabus

Unit-1. Fundamental of Artificial Intelligence, history, motivation and need of AI, Production systems, Characteristics of production systems , goals and contribution of AI to modern technology, search space, different search techniques: hill Climbing, Best first Search, heuristic search algorithm, A* and AO* search techniques etc.

Unit-2. Knowledge Representation, Problems in representing knowledge, knowledge representation using propositional and predicate logic, comparison of propositional and predicate logic, Resolution, refutation, deduction, theorem proving, inferencing, monotonic and non-monotonic reasoning.

Unit-3. Probabilistic reasoning, Baye's theorem, semantic networks, scripts, schemas, frames, conceptual dependency, forward and backward reasoning.

Unit-4. Game playing techniques like minimax procedure, alpha-beta cut-offs etc, planning, Study of the block world problem in robotics, Introduction to understanding, natural language processing (NLP), Components of NLP, application of NLP to design expert systems.

Unit-5. Expert systems (ES) and its Characteristics, requirements of ES, components and capability of expert systems, Inference Engine Forward & backward Chaining, Expert Systems Limitation, Expert System Development Environment, technology, Benefits of Expert Systems.