Unit I
Introduction: Expert systems and their history, Expert systems in daily life, Case study of expert systems. Emulation of human cognitive process, knowledge search trade-off, stored knowledge, semantic nets. An abstract view of modeling, elementary knowledge. Computational logic, analysis of compound statements using simple logic connectives, predicate logic, knowledge organization and manipulation, and knowledge acquisition.
Unit II
Search methods and knowledge representation: Introduction to Fuzzy logic with examples, Bayesian probabilistic inference, possible world, representation, Structure knowledge: Graph, frames, and related structures. Object-oriented, representation- object classes, messages, and methods. Search and control strategies - Concepts, search problems, searching AND – OR graphs.
Unit III
Knowledge organization and communication in expert systems: Knowledge organization- Indexing and retrieval techniques, integration of knowledge in memory organization systems, Perception and communication in expert systems. Overview of Linguistics, Basic passim techniques, semantic analysis and representation structures, natural language generation, and system.
Unit IV
Pattern recognition and learning techniques: Pattern recognition system- understanding speech recognition, Image transformation, low-level processing, medium and high-level processing, vision system architecture, Rule-based system architecture, knowledge acquisition and validation, knowledge system building tools
Textbooks:
- Russel (Stuart), ‘Artificial Intelligence- Modern approach, Pearson Education series in AI’, 3rd Edition, 2009.
- Dan W Patterson, ‘Introduction to Artificial intelligence and Expert systems’, Prentice Hall of India Pvt. Ltd,2001
Reference Books:
- Eugene Charniak, Drew Mc Dermot, ‘Introduction to Artificial intelligence’, Addison Wesley Longman Inc.,2009
- George. F, William. A. Stubblefield, ‘Artificial intelligence and the design of expert systems’, The Benjamin Cummins Publishing Co., Inc 2nd Edition, 1992.
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