Artificial Intelligence

by Bhuvaneswari, B, E M Roopadevi & S.Anandamurugan

ISBN: 9789390259113
View Ebook
Imprint : Associated Publishing Company
Year : 2021
Price : Rs. 5495.00
Biblio : x+109

Author Profile

Ms. B. Bhuvaneswari working as an Assistant Professor in the Department of Information Technology of Kongu Engineering College, Perundurai. She received her Bachelor degree in Computer Science and Engineering and Master degree in Network Engineering from Anna University, Coimbatore. She has been in the teaching profession for the past eleven years. Her areas of academic interest include Wireless Networks and Wireless Communication Networks. She published five articles in International Journals and presented five papers in International and National Conferences. E. M. Roopadevi received the B.E. (Electrical and Electronics) and M.E. (Computer Science and Engineering) degrees from Anna University. She has 8 years of experience in teaching field. Her research interests are in the areas of Network Security and Data Mining. Currently she is working as an Assistant Professor, Kongu Engineering College and pursuing the Ph.D in Computer Science from Anna University. Dr. S. Anandamurugan obtained his Bachelor's degree in Electrical and Electronics Engineering from "Maharaja Engineering College, Avinashi under Bharathiyar University and Master degree in Computer Science and Engineering from "Arulmigu Kalasalingam College of Engineering, Krishnan Koil under Madurai Kamaraj University. He completed his Ph.D in Wireless Sensor Networks under Anna University, Chennai. He has 17 years of teaching experience. He is currently working as an Assistant Professor (Selection Grade) in the Department of Information Technology, Kongu Engineering College.

About The Book

Artificial Intelligence is an indispensable text for teaching and learning artificial intelligence. It focuses to study the concepts of artificial intelligence. It helps to learn the methods of solving problems using artificial intelligence. It also introduce the concepts of expert systems and machine learning. Features: P Student friendly –written in a clear, concise and lucid manner P A sincere attempt has been made to introduce the basic concepts P Each chapter is organized into small sections that address key topics P In-depth coverage and good style are emphasized P Motivates the unmotivated P Explains the philosophy behind the artificial intelligence P Covers both elementary as well as advanced concepts P Aids understanding of concepts by providing diagrams and program listings wherever appropriate P Logical flow of concepts starting from the preliminary topics to the major topics

Table of Contents

Acknowledgement v Preface vii 1. Introduction to AI and Production Systems 1 Introduction to AI-Problem formulation, Problem Definition - Production systems, Control strategies, Search strategies. Problem characteristics, Production system characteristics - Specialized production 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. 2. Representation of Knowledge 29 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. 3. Knowledge Inference 55 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. 4. Planning and Machine Learning 69 Basic plan generation systems -Strips -Advanced plan generation systems –K strips -Strategic explanations -Why, Why not and how explanations. Learning- Machine learning, adaptive Learning. 5. Expert Systems 91 Expert systems - Architecture of expert systems, Roles of expert systems -Knowledge Acquisition - Meta knowledge, Heuristics. Typical expert systems - MYCIN, DART, XOON, Expert systems shells. Index 111