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AI Techniques for Game Programming (Premier Press Game Development), by Mat Buckland
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"AI Techniques for Game Programming" takes the difficult topics of genetic algorithms and neural networks and explains them in plain English. Gone are the tortuous mathematic equations and abstract examples to be found in other books. Each chapter takes readers through the theory a step at a time, explaining clearly how they can incorporate each technique into their own games. After a whirlwind tour of Windows programming, readers will learn how to use genetic algorithms for optimization, path-finding, and evolving control sequences for their game agents. Coverage of neural network basics quickly advances to evolving neural motion controllers for their game agents and applying neural networks to obstacle avoidance and map exploration. Backpropagation and pattern recognition is also explained. By the end of the book, readers will know how to train a network to recognize mouse gestures and how to use state-of-the-art techniques for creating neural networks with dynamic topologies.
- Sales Rank: #376095 in Books
- Brand: Brand: Cengage Learning PTR
- Published on: 2002-10-14
- Original language: English
- Number of items: 1
- Dimensions: 1.01" h x 7.36" w x 9.26" l, 1.75 pounds
- Binding: Paperback
- 480 pages
- Used Book in Good Condition
Review
"Game programming is without a doubt the most intellectually challenging field of computer science in the world. However, we would be fooling ourselves if we said that we are 'serious' people! Writing (and reading) a game programming book, should be an exciting adventure for both the author and the reader." Andre LaMothe, Series Editor
About the Author
Mat Buckland studied Computer Science at London University, then spent many years as a Risk Management Consultant. He went on to work for a developer producing games fro Gremlin Software. Buckland now works as a freelance programmer and AI consultant. He has been interested in evolutionary computing and AI in general since he first read about these techniques back in the early 80¿s. He is the author of the ai-junkie.com web site (www.ai-junkie.com), which provides tutorials and advice on evolutionary algorithms.
Most helpful customer reviews
6 of 6 people found the following review helpful.
Perfect for me
By A Customer
I spent the majority of the time I was reading this book thinking "This is perfect!" Just about every other book of this general type that I have read assumes I know things I don't, is a lot thicker than it needs to be, is much too simple, or is really boring. I don't think the author of this book wasted any pages at all. Everything was put forth in a concise, easy to read tone, and whenever I came across something I hadn't seen before, it was explained in short order. At the same time, he does assume that you know what you're doing, C++ programming-wise, so I didn't have to skip past anything I already knew. For what it's worth, this book created a lot of enthusiasm and confidence in me.
The examples in the book are great. Each chapter takes you through a different small project that helps you to understand what is going on, as well as how it can be applied to games. And, as a bonus, the projects are actually interesting! I really got a kick out of showing my versions of them to people. I was able to code each of them myself in a day or two as I went, but the author's full source code and executables are included if you just want to take a look as you read.
If you are already pretty familiar with windows programming, you probably won't need to read the first two chapters, but they answered a lot of questions for me, and really I felt like the author knew what I was thinking. Later on in the book, there is a review of transforms and matrix math that I found to be a really good reference and refresher.
As a side note, the author wanted to title this book "Genetic Algorithms and Neural Networks for Game Programming," but his publisher didn't think it was 'snappy' enough. He's very active in the forums on his website, and he and others there have been a great help to me.
3 of 4 people found the following review helpful.
Not Even Dated in 2010-- Buy It!
By Let's Compare Options Preptorial
I know what you're thinking: Heck, a book published in the early 2000s with reviews in 2005 has to be pretty dated as we look at 2010 and beyond. Wrong! Sure, most AI programmers have moved past Win 32 with numerous new techniques in C++ and Java, and the author's subsequent book (Programming Game AI by Example Programming Game AI by Example) is outstanding in filling in details left out for beginning and intermediate programmers here. However, TGP has all the makings of a classic, and if you miss it, your AI library will have a glaring and lonely hole. Our aeronautic simulation group at xtmh dot com hires numerous fresh grads from quality practical schools like Full Sail as well as quality abstract schools like MIT. What we find is that students who have grounded themselves in Buckland's two books, then evolved by staying up to date with his refresing openness to communication and support, have an outstanding balance of higher math skills (like quadratic programming, tensors and vectors) and practical "biological" agent motivation wisdom. Let's take another example: assume you love game design, but your skill set is in writing, dialog and character development, not MIT-level tensor mapping. Let's also say you've read the wonderful ultimate guide to writing and design (The Ultimate Guide to Video Game Writing and Design [ULTIMATE GT VIDEO GAME WRI -OS]. You are a skillful character, situation and dialog writer and designer, and will likely find a welcome place in supercomputing, the game industry, entertainment, or even the rapidly integrating fields (thank you pixar) of film/TV/gaming. To be successful and at the top of your game, you'll need to communicate your characters' motives, flaws, quests, wins and losses (a lot like real life) to the AI geniuses who will execute your vision in code. BOTH of Dille's books are written at a basic enough level to help you start translating your characterizations into agent behaviors. The opposite also is true-- if you're more of a codie, you'll love opening your mind to thinking at a more agent motivational level, with both time tested and new models of behavior in varying situations. Let's be honest-- the games of 2020 and beyond are all about intelligent agent interaction. The oldest problem in gaming-- how to dumb down a smart hero character enough to make them need a quest, but at the same time not make them look like idiots (stop, I don't want to hear about amnesia), has a corrolary in real life: sure, we could have been made smart enough to know all of life by just hacking our own brain, but God and our own Higher Self User put this odd dichotomy of an unconscious brain able to do array processor and direct geometric tensor mapping-- basically matrix calculus of partial derivatives-- something no supercomputer can do yet (we still have to convert geometric matrices and tensors to numeric models for processing even in supercomputers)-- when doing as simple an act as crossing a street in traffic; with a "conscious" brain that sometimes has trouble with four function math! If you need to figure out the paradox of dumbing down a genius in your character development, and the dicotomy seems too far fetched-- simply check out your average friend, or look in the mirror! Is this a digression? Nope-- we're just hinting at how cool a combination you will be able to imagine if you combine your study of data structures and algorithms with the now well established multi agent AI techniques. A hint at the future: we're finding clients as sophisticated as NASA and Los Alamos looking for new models of AFFECTIVE programming right now. Meaning, characters that not only learn and think as they move through your sims, but also are motivated by that other squishy reality-- heart. So the critics who think Buckland is out of date or too simple-- map your tensor into another frame of reference-- that of bridging code with biological and emotional motivation-- and you'll see why the most interesting games, sims and even real life "models" of cellular and synaptic structure and function need BOTH high level math and fun and interesting situational behaviors for the best games, and lives.
1 of 1 people found the following review helpful.
Good practical intro
By GPRed
A good introduction to genetic algorithms and neural networks, which are the only AI techniques this book covers. You should walk away from this book ready to implement these techniques via your own ideas.
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