M. Tim Jones has been developing software since 1986. He has published articles on embedded systems, network protocols, and artificial intelligence for Dr. Dobb’s Journal, Embedded Systems Programming, and Embedded Linux Journal. In addition, he is the author of GNU/Linux Application Programming and TCP/IP Application Layer Protocols for Embedded Systems. He resides in Longmont, CO, where he works as a Senior Principal Software Engineer.

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.

The popularity of artificial intelligence continues to grow as more and more uses are found for the technology. AI Application Programming Second Edition is completely updated to supply both the conceptual background and the real-world examples needed to begin using AI in software projects. Each technology is illustrated with a model implementation and application, and complete source code for each example is provided on the companion CD-ROM. Selected applications cover data mining, genetic algorithms, game programming, embedded rules-based engines, and the World Wide Web.

KEY FEATURES:
* Covers cutting-edge AI concepts such as neural networks, natural language processing, intelligent agents, genetic algorithms, rule-based systems, unsupervised learning algorithms, migratory software, and more
* Teaches each AI concept through a practical application, including a financial data miner, a Web spider, a networked data collector, a game program, an embedded battery charger control system, an embedded rules-based engine for log monitoring, and a fault tolerance subsystem
* Groups AI topics by conceptual subfields (machine learning, evolutionary methods, symbolic methods) for better "big picture" understanding and more focused specialization
* Provides a background in the history of AI, the distinct branches of this broad field, and the philosophical underpinnings and issues associated with these technologies
* Includes a CD-ROM (Win/Linux) with complete, fully commented source code in C for every application in the book
* Exercise sets for each chapter are located in Appendix A for use as a Textbook

AI Application Programming

This book gave me a head start on AI with hands-on examples on different techniques I could use in many different problems. this might be one of the best books if you want to start with AI yet don’t want to get involved with the horrifying math behind it. it just directly jumps to the fun stuff :D

This Book Exceeds My Expectations

It’s just amazing how such a 473-page book could cover wide range of AI subjects, and at the same time could deliver easy-to-understand explanations without throwing PhD maths at you. It is just superb how a tech book with minimal math could explain everything!

Every chapter covers one kind of AI, one or two real-world applications and a list of other applications. There’re more than enough illustrations for people like me who hate text-only book. A picture is worth a thousand words buddy. Here’s my list of AI fields I remembered from the book: Genetic Algorithm, Neural Network, Ant, Particle Swarm, Simulated Annealing, ALife, Pathfinding, A-Star, ATR1, Classifier systems, Rule-based Aystems, Agent-based Software, NLP, Bigram, and Fuzzy Logic.

Examples are written in C language. Comments are plentiful. The codes, though short, pack a lot punches; a whole stack-only virtual machine (VM), for example, was written in less than 100 lines to illustrate how Genetic Algorithm works. Although I’m a VB.NET programmer, I could understand them quite easily.

I recommend this book to any programmer who like to get a big picture of artificial intelligence, who doesn’t know where to begin or which algorithm is the right choice. This book should be the first-to-read but not the last, because it touches not far from the surface. I’d say this is the best precursor.

Decent introduction, insane amount of typos

The code is fairly easy to read, and (to date) I have not found any errors in example code... but in terms of the author’s ability to write properly, that’s a whole different story. Formulas are missing halfs, inconsistencies in variable usage, it’s seriously bad. Maybe I got a bad edition (didn’t buy from Amazon), but it looks exactly like the one listed (softcover, 2nd ed)..

If you’re fairly proficient at C/C++ code, however, you should be able to follow the book ok... just expect to be reading more of the source code than the actual writing, half the time.

It was a required textbook for a class.

Best introductory book on AI

It is the best introductory book on AI. As you can read from other reviewer’s with low rating, it’s not in depth. But if you know programming already, and want to know AI, this is a good start.

Not very clear

This book covers a good range of topics but fails to deliver the topics in a clear and concise manner. Not to mention there are many typos (including incorrect and incomplete equations). He also tries to give large examples where a small example would be more suitable. He also does not finish some of the examples (such as the A* method example).

Excellent Introduction (w/out alot of number theory)

One of the challenges in learning AI are the foundational requirements in mathematics. This book provides an excellent introduction to AI, where the "hacker" or hobbyist can sit-down and be writing simple AI applications in the matter of minutes. If your looking for a practical hands-on AI book I would start here.

The book has its values, but also got serious problems

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.

Great second edition of an applied book on AI

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

GREAT text for "real world" developers who want more from their apps

One of the major reasons I wrote, "Building Intelligent .NET Applications" was the inspiration I received from reading the first edition of M. Tim Jones book titled, "AI Application Programming". It was the first book I had ever come across that presented AI Programming in a way that professional developers like me could easily absorb. So, I was thrilled when I saw that in 2005, Mr. Jones had released a second edition of his excellent book.

In the second edition, Mr. Jones extends his coverage of cutting edge AI topics and includes juicy topics such as neural networks, fuzzy logic, natural language processing, and reinforcement learning. All of this is demonstrated through well written text and practical C code included on the books CD.

The reason this book is so brilliant is that it takes all the AI concepts that most developers are scared to touch and shows that they are really not that difficult. As opposed to the usual overpriced and stuffy academic texts that include way too much math and theory and not enough actual code, Mr. Jones presents the material in a very intuitive way.

In the preface Mr. Jones writes: "My goal in writing this book is to demystify some of the more interesting AI algorithms so that a wider audience can use them. It’s my hope that through the detailed discussions of the algorithms in this book, AI methods and techniques can find their way into more traditional software domains."

In my humble opinion, any software developer today that wants to stay ahead of the curve NEEDS to get this book and start applying the techniques as Mr. Jones suggests. I believe he will succeed in his goal of opening AI up to a wider audience.

Second Edition Expanded and Improved

Artificial Intelligence (AI) began to be worked on by the computer professionals (and Hollywood) many years ago. The professionals have not been able to catch up with the Hollywood types. Still remarkable progress has been made in the field. This book, now in its second edition, is an intermediate level book that discusses the so called Weak AI. This is AI that is integrated into an application and usually is no longer called AI but something else like fuzzy logic.

AI as it is practiced today can be viewed as a series of algorithms that handle the application tasks being performed by the AI module. Generally speaking each chapter in this book covers one of the algorithms. The chapter contains a description of the application, how the concept applies, and gives example code to perform the job.

The CD supplied with the book contains a number of useful applications that demonstrate the properties of AI Algorithmic techniques and methods.

Outstanding book and Insightful Introduction to AI

All main directions of AI (Neural Networks, Genetic Algorithms, Ant Colony Optimization, Adaptive Resonance Theory, Artificial Life and many more) are covered in concise and clear manner.
What makes this book outstanding is that application examples which follow theoretic material are considered in greater detail; all applications of AI techniques presented in this book are very well selected and are all insightful.
This book is also very well organized. Short introduction presents an insightful position on the issue; the intro is followed by application example where explanation of issues continues on practical matter.
Algorithms for all topics are written very clearly, and the code is transparent.
In summary, this book is excellent introduction to AI which not only clearly identifies important features of various AI techniques and develops ideas clearly, but also effectively supports it with excellent examples.

not really what I was searching for....

I suppose this book would be good for those wanting at best a cursory glance at the world of AI (I applaud the author for the breadth of material covered), but for those looking for any practical grounding into AI, this book falls sadly short. There are examples in each chapter, but it seems that what theory is explained is just barely enough to get you by to understand them.

I though the book would be more in-depth. Plus there are several annoying math slip-ups.

Nice attempt

This is a basic overview of AI programming with a focus on applications. I commend the author for his efforts, but still recommend AI Game Programming Wisdom instead.

Perfect Beginner AI book

Gives concrete explanation of each algorithm and real examples. Most other AI books were a blur until I read this one.