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ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

Course Content:

• First Month - UNIT I INTRODUCTION TO Al AND PRODUCTION SYSTEMS
Introduction to AI-Problem formulation, Problem Definition -Production systems, Control strategies,sSearch 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.

• Second Month - UNIT II 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.

• Third Month - UNIT III 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.

• Fourth Month - UNIT IV 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.

• Fifth Month - UNIT V 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.

Course Features

  • Duration : 80 Hours
  • Lectures : 40
  • Quiz : 20
  • Students : 15

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