- Paperback: 500 pages
- Publisher: Charles River Media; 2nd Revised edition edition (3 July 2005)
- Language English
- ISBN-10: 1584504218
- ISBN-13: 978-1584504214
- Product Dimensions: 23.4 x 18.8 x 3.1 cm
- Average Customer Review: 3.5 out of 5 stars See all reviews (2 customer reviews)
- Amazon Bestsellers Rank: 1,052,966 in Books (See Top 100 in Books)
- See Complete Table of Contents
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More About the Author
Product Description
Product Description
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.
About the Author
M. Tim Jones (Longmont, CO) has been developing software since 1986. He has designed prototype AI systems using genetic algorithms for satellite attitude determination and mobile agents for distributed asset tracking. He has also published articles on embedded systems, network protocols, and AI for Dr. Dobb's Journal, Embedded Systems Programming and Embedded Linux Journal. In addition, he is the author of BDS Sockets Programming from a Multi-Language Perspective and TCP/IP Application Layer Protocols for Embedded Systems. He resides in Longmont, CO, where he works as a Senior Principal Software Engineer.
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Customer Reviews
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Most Helpful Customer Reviews
5 of 7 people found the following review helpful:
4.0 out of 5 stars
This is a good book with many examples of C language code.,
This review is from: AI Application Programming (Charles River Media Programming) (Paperback)
This book will give all the basic idea about how to program in many ways of AI such as neural network, genetic algorithm and fuzzy logic. Everything is explained and followed by the example code.These examples will make a better view and understand of what you should do when you want to program in AI field.I thought this book will help all guys who want to deal with such kind of computer programming. Exellent!
2 of 3 people found the following review helpful:
3.0 out of 5 stars
The first half was excellent,
By A Customer
This review is from: AI Application Programming (Charles River Media Programming) (Paperback)
A very good and practical first introduction to a fascinating subject. He covers a lot of interesting algorithms in a non-technical way, yet manages to convey the essence of how they work. The book left me wanting to find more about some of the methods that interested me most, but did not really include enough references. If you want a rigorous introduction, this is not the book for you. The applications get steadily more complex, and in my view the best ones are in the first half of the book. The sections on neural nets, genetic algorithms and artificial life were exceptionally interesting. After that I felt chapters went downhill in terms of the elegance of the algorithms and interest of the applications. There's lots of C code in there, and this was just about well annotated enough for me (a non-C programmer) to follow.
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Most Helpful Customer Reviews on Amazon.com (beta) Amazon.com:
4.4 out of 5 stars (19 customer reviews) 21 of 22 people found the following review helpful:
5.0 out of 5 stars
Great, in depth, recursively precise!,
By T. Fitzpatrick "Thornite" - Published on Amazon.com
This review is from: AI Application Programming (Charles River Media Programming) (Paperback)
I enjoyed working through this text, but not without some re-visiting of my calculus classes and trigonometry brush-ups.All in all a very good book, and also a great Graduate level reference for the inner workings of actual Artificial Intelligence algorithms. If you are well prepared, this book is to the point, and well worth the read. Prepare for a visit to College-level Physics theorems, as many algorithms given require a working knowledge of the advanced principles of the science. Hope this helps- 15 of 15 people found the following review helpful:
3.0 out of 5 stars
The book has its values, but also got serious problems,
By Oliver - Published on Amazon.com
This review is from: AI Application Programming (Charles River Media Programming) (Paperback)
Most of other reviewer think highly of this book. I also agree, to a certain extent, that the book's is valuable and fill in the gap between "talks" and "walks".
However, there are two things I have to point out: One, the editing/basic correctness check of this book is kinda terrible. For example, P72 on Particle Swarm Optimization, the 4.2 formula is obviously WRONG and not consistent with the rest of discussion. Also on P74, the position vector calculation is wrong as well: it also seems the author/editor cut & copy two blocks of text. Second, I don't like is the lack of explaination on certain important notations and equations, which is very important to be at least "self-contained" for such a "cover everything" book. For example, P210 on reinforcement learning, Equation 9.2 has a general explaination of what it is, but non of those notation/symbols in the equation make sense in the context. So, in general, be aware its pro and cons. 19 of 21 people found the following review helpful:
5.0 out of 5 stars
Great second edition of an applied book on AI,
By calvinnme - Published on Amazon.com
This review is from: AI Application Programming (Charles River Media Programming) (Paperback)
Scientists started the field of AI research in the 1950's with the now largely failed quest to produce machines that think. However, they did open the door to making improved individual products that can "learn" how to do their limited jobs better, and they also opened the door to the use of AI in games and in recommender systems such as you see here on Amazon.
This book is the second edition of the successful book by Tim Jones on different facets of AI, how they can be used, and how to write programs that implement the necessary algorithms. The book begins with a short but insightful chapter on the history of AI, followed by a series of chapters, each covering a specific AI technique. The last chapter covers the state of AI today. Each chapter begins with a short description of the technique covered, sometimes including parallels to the real world that are behind the algorithmic choices of the technique. Next, the algorithm is described, and a sample implementation is given and discussed. Last, the author presents examples of problems that can be solved by the given technique. This book basically replaces the first edition, as everything in that book is in this one plus the A* pathfinding algorithm, particle swarm optimization, classifier systems, reinforcement learning, and natural language processing. For several of the techniques variations and tuning opportunities are presented, allowing the reader/programmer to easily adapt the technique to a different problem of a similar type. There are also plenty of illustrations and diagrams, making the material easier to absorb. I highly recommend that you purchase this second edition, even if you already have the first edition. It is a worthwhile upgrade. The author assumes that the reader has already been exposed to the basic ideas of artificial intelligence and is proficient at programming in C. I notice that Amazon does not show the table of contents for the 2nd edition, so I do that here. 1. History of AI 2. Pathfind and the A-Star Algorithm ** 3. Simulated Annealing 4. Particle Swarm Optimization ** 5. Introduction to Adaptive Resonance Theory (ART1) 6. Classifier Systems ** 7. Ant Algorithms 8. Introduction to Neural Networks and the BackPropagation Algorithm 9. Introduction to Reinforcement Learning ** 10. Introduction to Genetic Algorithms 11. Artificial Life 12. Introduction to Rules-Based Systems 13. Introduction to Fuzzy Logic 14. Natural Language Processing ** 15. The Bigram Model 16. Agent-Based Software 17. AI Today ** Denotes a totally new chapter |
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