Artificial Intelligence (A Conceptual Approach)

By Anamitra Deshmukh-Nimbalkar more
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Product Specifications

Publisher Technical Publications All Computer Science books by Technical Publications
ISBN 9789350992647
Author: Anamitra Deshmukh-Nimbalkar
Number of Pages 521
Edition First Edition
Available
Available in all digital devices
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Artificial Intelligence (A Conceptual Approach) - Page 1 Artificial Intelligence (A Conceptual Approach) - Page 2 Artificial Intelligence (A Conceptual Approach) - Page 3 Artificial Intelligence (A Conceptual Approach) - Page 4 Artificial Intelligence (A Conceptual Approach) - Page 5

Artificial Intelligence (A Conceptual Approach) by Anamitra Deshmukh-Nimbalkar
Book Summary:

The importance of Artificial Intelligence is well known in various engineering fields. Overwhelming response to my corporate world software training material, notes and books on various subjects inspired me to write this book. The book is structured to cover the key aspects of the subject Artificial Intelligence.

The book uses plain, lucid language to explain fundamentals of this subject. The book provides logical method of explaining various complicated concepts and stepwise methods to explain the important topics. Each chapter is well supported with necessary illustrations, practical examples and solved problems. All the chapters in the book are arranged in a proper sequence that permits each topic to build upon earlier studies. All care has been taken to make students comfortable in understanding the basic concepts of the subject.

Audience of the Book :
This book Useful for MCA & Computer Science Engineering student.
Table of Contents:

1. Introduction to AI

2. Problem Solving

3. Informed Search

4. Constraint Satisfying Problems and Adversarial Search

5. A Logical Agent and it's Language of Knowledge

6. First Order Logic - A Expressive Language for Knowledge Representation

7. Knowledge Representation and Ontological Engineering

8. Planning in Classical Environments

9. Planning In Non-Classical Environments

10. Uncertain Knowledge and Role of Probability Theory

11. Temporal Models

12. Learning Agent and Knowledge in Learning

13. Statistical Learning

14. Reinforcement Learning

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