AI Application Programming / Edition 2

AI Application Programming / Edition 2

by M. Tim Jones, Jones
     
 

In the second edition of this bestseller, the author continues to demystify the techniques associated with the field of artificial intelligence. It covers a wide variety of techniques currently defined as "AI" and shows how they can be useful in practical, everyday applications. AI Application Programming covers both the theory and the practical applications to

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Overview

In the second edition of this bestseller, the author continues to demystify the techniques associated with the field of artificial intelligence. It covers a wide variety of techniques currently defined as "AI" and shows how they can be useful in practical, everyday applications. AI Application Programming covers both the theory and the practical applications to teach developers how to apply AI techniques in their own designs. Each chapter covers both the theory of the algorithm or the technique under discussion followed by a practical application of the technique with a detailed discussion of the source code.

Product Details

ISBN-13:
9781584504214
Publisher:
Cengage Learning
Publication date:
06/03/2005
Edition description:
Book and CD
Pages:
473
Product dimensions:
7.40(w) x 9.30(h) x 1.08(d)

Meet the Author

M. Tim Jones is an embedded software architect and the author of numerous books, including AI Application Programming, Second Edition (Charles River Media), BSD Sockets Programming from a Multilanguage Perspective (Charles River Media), Artifi cial Intelligence: A Systems Approach, and many articles on a variety of technical subjects. His engineering background ranges from the development of kernels for geosynchronous spacecraft to embedded systems architecture and networking protocols development. Tim is a consultant engineer for Emulex Corp. in Longmont, Colorado.

Table of Contents

History of AI What is AI? Strong and Weak AI The Result of AI AIs Modern Timeline Branches of AI Key Researchers Philosophical, Moral, and Social Issues Structure of this Book 2 Pathfinding and the A-Star Algorithm 3 Simulated Annealing 4 Particle Swarm Optimization 5 Introduction to Adaptive Resonance Theory (ART1) 6 Introduction to Classifier Systems 7 Ant Algorithms 8 Introduction to Neural Networks and the Backpropagation 9 Introduction to Reinforcement Learning 10 Introduction to Genetic Algorithms 11 Artificial Life 13 Introduction to Fuzzy Logic 14 Natural Language Processing 15 The Bigram Model 16 Agent-based Software 17 AI Today References Resources Appendix A Exercises and Problems Appendix B About the CD-ROM

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