Artificial Intelligence
- Introduction to AI and Production Systems: Introduction to AI- Problem formulation, Problem Definition - Production systems, Control strategies, Search strategies. Problem characteristics, Production system characteristics - Specialized productions system- Problem solving methods – Problem graphs, Matching, Indexing and Heuristic functions - Hill Climbing, Depth first and Breath first, Constraints satisfaction – Related algorithms, Measure of performance and analysis of search algorithms.
- Representation of Knowledge: Game playing – Knowledge representation, Knowledge representation using Predicate logic, Introduction to predicate calculus, Resolution, Use of predicate calculus, Knowledge representation using other logic-Structured representation of knowledge.
- Knowledge Inference: Knowledge representation - Production based system, Frame based system. Inference – Backward chaining, Forward chaining, Rule value approach, Fuzzy reasoning – Certainty factors, Bayesian Theory, Bayesian Network- Dempster – Shafer theory.
- Planning and Machine Learning: Basic plan generation systems – Strips - Advanced plan generation systems – K strips - Strategic explanations Why, Why not and how explanations. Learning- Machine learning, adaptive Learning.
- Expert Systems: Expert systems – Architecture of expert systems, Roles of expert systems – Knowledge Acquisition – Meta knowledge, Heuristics. Typical expert systems – MYCIN, DART, XOON, Expert systems shells.