Including Bibliographic details and Index

1. Artificial Intelligence Fundamentals

UNIT 1: PROBLEM SOLVING

2. State-Space Searches

3. Problem Reduction and Game Playing

UNIT 2: LOGIC CONCEPTS AND LOGIC PROGRAMMING

4. Logical Reasoning

5. Prolog Programming

UNIT 3: PLANNING, KNOWLEDGE REPRESENTATION AND EXPERT SYSTEM

6. Advanced Problem-Solving Paradigm: Planning

7. Knowledge Representation

8. Expert System and Applications

UNIT 4: HANDLING UNCERTAINTY AND FUZZY KNOWLEDGE

9. Uncertainty Measure: Probability Theory

10. Fuzzy Sets and Fuzzy Logic

UNIT 5: MACHINE LEARNING PARADIGMS

11. Machine Learning

12. Artificial Neural Networks

13. Evolutionary Computation

UNIT 6: TRADITIONAL AND LATEST PERSPECTIVES OF NATURAL LANGUAGE PROCESSING (NLP)

14. Traditional NLP

15. NLP Application Pipeline

16. Advanced NLP with Deep Neural Networks

This second edition of Artificial Intelligence is designed to provide comprehensive material for undergraduate and graduate students on the vast and fast-growing subject of Artificial Intelligence. This book has been written keeping in mind the syllabi designed for courses on AI in various technical institutions and universities in India and abroad. It can serve as a textbook for one full-semester course on AI. It will also provide study material to computer professionals who wish to expand their knowledge on AI.

The book is divided into 6 units with each unit containing related chapters. There are overall 16 chapters. Compared with the syllabi of UG programmes in Indian universities, there is about 40% extra material that might be useful for advanced AI courses. The main topics covered in the book include problem-solving using intelligent searches and planning, knowledge representation techniques, game playing, first-order predicate logic and programming language PROLOG (programming in logic), uncertainty handling, expert systems, knowledge representation, etc. The language PROLOG (declarative language) has been used throughout the book which I am very passionate about as it helps user to code programs for problems using AI techniques with a clear understanding of underline and natural thought processes of human ways of solving problems.

In addition, some advanced topics such as machine learning, fuzzy logic, artificial neural networks, evolutionary computing, and natural language processing have been included in detail. Each chapter in the book has been carefully developed with the help of several pedagogical features. A huge effort has been put in to explain every concept discussed in the book with the help of examples as far as possible. Pseudo-algorithms for various methods and techniques are included throughout the text to increase the comprehensibility of the topics and demonstrate their applications.