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.

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.

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

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.

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.

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.

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.

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

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-

Re the comments below, I contact the publisher on the Internet and ask if they had a erratum sheet. In less than a day, a copy was sent to me, and they also have those sheets onsite for most of their publications. Additionally, a second edition was released in October which corrected the identifiable errors. A class act in my opinion.

This book is a great idea. I am enjoying reading it and working through the examples. I get hooked on each topic, and I find myself absorbed in learning the algorithms and playing with the code. The code is readable, and the author explains the code well.

Unfortunately, there are some errors in this book. I originally complained in my review about not having found any errata on the publisher's site. It turns out they are there now. Last I checked, you get to the errata from the main page, not from the book's page. I am corresponding with the author about some errors that haven't been caught yet. He has been very responsive.

Once the errors get caught and fixed, this will be an amazing book. I don't know of any other quite like it.

Artificial intelligence has grown by leaps and bounds in the last 50 years, but during this time it has also seen a lot of valleys and backwashes, especially in the field of robotics. The unrelenting obsession, driven mostly by military needs, for creating autonomous thinking robots has met with considerable disappointment in the last few decades. This has caused some researchers to distance themselves from the words "artifical intelligence" in order to regain the confidence of funding sources. Thus one hears the words "computational intelligence" or "cognitive science" to describe the field. But sometimes words can accurately describe concepts or properties even they were chosen somewhat cavilierly. "Computational intelligence" could thus be viewed as that branch of artificial intelligence which primarily deals with algorithms designed to deal with large amounts of data, finding interesting and nontrivial patterns thereof.
The content of this book could be viewed as a collection of algorithms in computational intelligence, but also includes topics not usually included in this classification, such as intelligent agents. Indeed, the concept of intelligent agents that the author discusses in the last chapter of the book draws on what he has done before it. The techniques and algorithms that he discusses in these chapters, such as neural networks, genetic algorithms, fuzzy logic, decision trees, and natural language processing, supply the decision-making capabilities for the intelligent agents. These intelligent agents can be viewed as a step towards resolving one of the major issues in artificial intelligence, namely of constructing intelligent software or machines that work in more than one domain. Playing good chess does not mean playing good poker, but developments in agent theory show promise in making expertise in both of these games a reality.

Some of the algorithms discussed in the book have their origins in physics (simulated annealing), biology (adaptive resonance theory, ant algorithms, genetic algorithms, and artificial life), and brain modeling (neural networks). The reader will also be introduced to some of the older methods in artificial intelligence, such as rule-based systems (loosely referred to as GOFAI for "good ole-fashioned artificial intelligence by some researchers). Source code in ANSI C is given for the algorithms, even though the resulting programs are command-line driven. In spite of this, and in spite of no use whatsoever made of Prolog or LISP, all of the chapters in this book will serve to introduce the beginning student or practicing scientist to useful algorithms in AI.

After many letdowns in the past five decades, and also many accomplishments, artificial intelligence is now taking off, and has invaded many different fields with a vengeance. Indeed, financial engineering, bioinformatics, network engineering, elementary particle physics, manufacturing, computer games, and many other fields are making heavy use of intelligent algorithms. A lot of this use has been driven also by the rise of the Internet and the dramatic increase in computational power of computer hardware. Even robotics, the field that has been a source of frustration for researchers in AI, has now shown every sign of finally moving ahead. Without a doubt the 21st century will see the presence of thinking machines. These may not take the form they do in popular entertainment, but in whatever context they are used, one will be able to trace their abilities to the painstaking and patient efforts of the many early researchers in AI. The minds of these machines, however exotic they may be, however advanced they may be, and however they are used, will be products of the incredible originality and skill of the human mind.

...in fact it is outstanding. This a modern text on AI. AI in my mind has been an oxymoron at best, and a terrible hoax at worst. Current AI had been little more than recursive interation through a problem set, but with focus on modifying the problem/solution set, and or algorithm in what was hoped an intelligent manner through each iteration to arrive at a solution. This is ofcourse a far cry from the early promise of AI, which the author handily acknowledges in the first chapter of the book detailing the history of AI.

The author has a clear goal with this book. Demystify the current state of the art, in the hopes that a baseline is established with the current generation of programmers with which to build on and possibly raise the bar.

This books captures a wide range of AI techniques (i.e. simulated annealing, adaptive resonance theory, ant algorithms, backpropagation algorithms, genetic algorithms/programming, artificial life/evolving neural networks, expert systems, fuzzy logic, hidden markov models, and intelligent agents) and provides ready to run and modify algorithms. The language chosen is C, with faint hints of lisp for some of the data sets. I found this a welcome change, as most hardcore AI books are heavy with the lisp, and heavy with the math. The C is simple, clear, and explained. There is some math, but it is no where near as overwhelming as it is in other AI books.

The analogies chosen are clear and spot on. After reading this book I felt smarter for having read it, and excited to practice some of the techniques learned. I even had thoughts of trying combinations of them for certain problems and also just for fun.

In short, the author has given me renewed hope for AI as a practical technology, and more importantly has not made me feel my money was ill spent. This book was a gamble for me, and I suspect it will be a gamble for others. Rest assured that it is worth the risk, even if you can't readily use any of the techniques it at the very least will spark the imagination and provide inspiration.