M.Tech I Year II Semester (R17)
Artificial Intelligence: Introduction, definition, underlying assumption, important of AI.AI & related fields State space representations, defining a problem, production systems and its characteristic, search and control strategies - Introduction, preliminary concepts, examples of Search problems.
UNI T - I I Uniformed or Preliminary Concepts: Examples of search problems, Uniformed or Blind Search, Informed Search, Or Graphs, Heuristic Search techniques - Generate and Test, Hill climbing, best first search, problem, reduction, constraint satisfaction, Means - Ends Analysis. Knowledge Representation Issues: Representations and Mapping, Approaches, Issues in Kr, Types of Knowledge procedural Vs Declarative, Logic programming. Forward Vs Backward reasoning, Matching, Non monotonic reasoning and it logic.
UNI T - I I I Use of Predicate Logic: Representing Simple facts, Instance and is a relationships, Syntax and Semantics for propositional logic, FOPL, and properties of Wffs, conversion to casual form, Resolution Natural deduction Statistical and Probabilistic Reasoning : Symbolic reasoning under uncertainly, Probability and Bayes' theorem, Certainty factors and Rule based systems, Bayesian Networks, Dempster - Shafer Theory, Fuzzy Logic.
UNI T - I V Expert Systems: Introduction, Structure and uses, Representing and using domain knowledge, Expert System shells. Pattern recognition, introduction, Recognition and classification process, learning classification patterns, recognizing and understanding speech.
UNI T - V Introduction to Knowledge Acquisition: Types of learning, General Learning model, and performance measures. Typical Expert Systems: MYCIN, Variants of MYCIN, PROSPECTOR, DENDRAL, PUFF etc. Introduction to Machine Learning: Perceptions, Checker Playing examples, Learning, Automata, Genetic Algorithms, Intelligent Editors.
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