Artificial Intelligence Simplified: Understanding Basic Concepts

by Binto George, Gail Carmichael, Susan S Mathai (Editor)

Overview

A small book that introduces key Artificial Intelligence (AI) concepts in an easy-to-read format with examples and illustrations.  A complex, long, overly mathematical textbook does not always serve the purpose of conveying the basic AI concepts to most people. Someone with basic knowledge in Computer Science  can have a quick overview of AI (heuristic searches, genetic algorithms, expert systems, game trees, fuzzy expert systems,  natural language processing, super intelligence,  etc.) with everyday examples. If you are taking a basic AI course and find the traditional AI textbooks intimidating, you may choose this as a "bridge" book, or as an introductory textbook.   (This is old edition -- a new revised edition is currently available.)

Product Details

ISBN-13:	9781944708023
Publisher:	CSTrends LLP
Publication date:	01/11/2016
Sold by:	Barnes & Noble
Format:	eBook
Pages:	138
File size:	6 MB

About the Author

Dr. Binto George is a professor in the School of Computer Sciences at Western Illinois University (WIU), Macomb, IL, USA. Before joining WIU, he worked at Rutgers University. Dr. George received his Ph.D. from the Indian Institute of Science, Bangalore. He has authored several journal articles, conference papers, book chapters, and books. As the principal investigator, Dr. George has led the National Science Foundation (NSF) funded research to incorporate usable security into the computer science curriculum. He loves teaching and developing new courses. Dr. George is a partner of the CSTrends LLP, an organization committed to making Computer Science accessible for all. Dr. George is a member of the IEEE, IEEE Computer Science Society, and the Association for Computing Machinery (ACM). Dr. George actively participates in community service and curriculum development activities.

Dr. Binto George is a professor in the School of Computer Sciences at Western Illinois University (WIU), Macomb, IL, USA. Before joining WIU, he worked at Rutgers University. Dr. George received his Ph.D. from the Indian Institute of Science, Bangalore. He has authored several journal articles, conference papers, book chapters, and books. As the principal investigator, Dr. George has led the National Science Foundation (NSF) funded research to incorporate usable security into the computer science curriculum. He loves teaching and developing new courses. Dr. George is a partner of the CSTrends LLP, an organization committed to making Computer Science accessible for all. Dr. George is a member of the IEEE, IEEE Computer Science Society, and the Association for Computing Machinery (ACM). Dr. George actively participates in community service and curriculum development activities.

Gail Carmichael is currently a technical educator at Shopify, where she led the design and launch of the work-integrated learning program Dev Degree. She previously worked as a full-time instructor at Carleton University, where she taught both majors and non-majors a variety of computer science courses. She is particularly passionate about teaching beginners and enticing them to fall in love with computer science, whether as a major or as a tool to help them in their own fields. She co-founded Carleton University's Women in Science and Engineering, helped launch the now Ontario wide Go Code Girl high school outreach program, and has developed and taught many computing workshops and courses for folks of all ages.

Table of Contents

1. Introduction 7 1.1. Organization 9 1.2. The Operating Room Scheduling Problem 11 1.3. Generate and Test 14 2. Scheduling with Search Methods 18 2.1. Blind Search Methods 22 2.2. Heuristic Search Methods 25 2.2.1 Hill Climbing 27 2.2.2 Best First Search 34 2.3. Best Path Methods 35 3. Accommodating Surprises with Planning Techniques 39 3.1. Forward Planning 41 3.2. Backward Planning 42 3.3. Partial-Order Planning 43 3.4. Planning Under Uncertainty 44 4. Evolving Schedules with Genetic Algorithms 47 4.1. Genetic Programming 53 5. Learning from Experience With Neural Networks 55 5.1. Multi-layer neural networks 61 6. Expert Systems for Diagnosis 64 6.1. Expert System Types 66 6.1.1 Forward chaining 66 6.1.2 Backward chaining 67 6.1.3 Hybrid chaining 68 6.1.4 Deduction and reaction systems 68 6.2. Fuzzy Expert Systems 69 7. Handling Competing Goals With Game Trees 72 8. Communicating With Natural Language 78 8.1. Natural Language Understanding 81 9. Identifying Intelligence 85 9.1. Super Intelligence 88 10. Conclusions and Where to Go From Here 90 10.1. AI and Other Disciplines 91 10.2. The Future of Artificial Intelligence 92 Appendix A: Search Methods 96 A.1. Depth First Search (DFS) 97 A.2. Breadth First Search (BFS) 98 A.3. Simple Hill Climbing 99 A.4. Steepest Ascent Hill Climbing 100 A.5. Best First Search 101 A.6. A* 102 Appendix B: Neural Network Learning More Complex Logic 104 Appendix C: Fuzzy Expert System 110 11. Bibliography 118 INDEX 132

Editorial Reviews

About the second edition:

The book introduces key AI concepts in an easy-to-read format with examples and illustrations. The goal is to keep the explanations as simple as possible. While the book includes some algorithms, flowcharts, and technical details, a reader should get an idea of basic AI concepts even if they don't go in-depth into those contents. A beginner may want to skip the depth content in your first reading.

For a professional and wish to get an overview of AI, this book provides some essential background. A robotics enthusiast wanting to understand the broader aspects of AI may find this book useful. A student taking an AI course may use this as an introductory textbook to develop a solid understanding of the basic AI concepts. This book includes numerous bibliographical references and resources that a reader may find useful for delving into more profound aspects of AI.

The first edition was very well received all over the world. The book received great reviews and encouraging feedback from readers, professionals, and well-wishers. As readers wanted, the second edition includes more relevant topics such as Generative Adversarial Network (GAN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), and artificial vision. A few readers wanted more depth content. So there is more advanced material without severely compromising the simplicity.

This title is listed as BookAuthority's "best AI Basics books of all time!"

From the Publisher