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Behavioral Mathematics for Game AI
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By Dave Mark Published March 2009 List Price: Amazon.com Sales Rank: 547,759 Availability: Usually ships in 24 hours Summary: Human behavior is never an exact science, making the design and programming of artificial intelligence that seeks to replicate human behavior difficult. Usually, the answers cannot be found in sterile algorithms that are often the focus of artificial intelligence programming. However, by analyzing why people behave the way we do, we can break down the process into increasingly smaller components. We can model many of those individual components in the language of logic and mathematics and then reassemble them into larger, more involved decision-making processes. Drawing from classical game theory, "Behavioral Mathematics for Game AI" covers both the psychological foundations of human decisions and the mathematical modeling techniques that AI designers and programmers can use to replicate them. With examples from both real life and game situations, you'll explore topics such as utility, the fallacy of rational behavior, and the inconsistencies and contradictions that human behavior often exhibits. You'll examine various ways of using statistics, formulas, and algorithms to create believable simulations and to model these dynamic, realistic, and interesting behaviors in video games. Finally, you'll be introduced to a number of tools you can use in conjunction with standard AI algorithms to make it easier to utilize the mathematical models. Review: Game developers often use little tricks to sprinkle magic decision-making abilities throughout their AI code, without necessarily understanding the fundamentals of how it works. Dave not only documents this process on paper, but he goes into the theoretical background behind these techniques. For anyone wishing to know more about the math behind common game behaviors, this is the ideal textbook on the subject. 'Alex J. Champandard Editor & consultant, AiGameDev.com Similar Books:
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1 Comments
This isn't a "gems" book. Most of the chapters build upon previously-learned material. Chapters include discussion of algorithmic modeling of realistic behavior, decision theory (including classical game theory and "prisoners dilemma" style problems). Later chapters move from theoretical to practical, showing how to model the conceptual stuff presented in earlier chapters into practical code that's usable in a game.
The book is mercifully free of sidebar discussions or freewheeling anecdotes. The book's hefty 450 pages is devoted entirely to the subject. When review-books arrive, I have a "one inch" rule. If the book is more than an inch thick, then it had better be a dense comprehensive guide to its subject. If the book got to its impressive length through lots of sidebar discussions and lots of screenshots of barely-related material, then we have a problem. Behavioral Mathematics for Game AI is free of both. All illustrations are necessary and illustrate the concepts presented. Code is also sparse and is mostly reduced to a few well-documented functions. The book hits the ground running, skipping the opening "how to program" chapters.
Most chapters have a "Putting It In Code" section that takes the conceptual materials you've learned and shows them as well-documented C++ code. The code is free of C++ esoterica, so it's very simple to write in another language. Behavioral Mathematics for Game AI would be as useful for Python or javascript or any other language as it is for C++.
As for the level, I'd place it as moderate. It's definitely approachable for a beginner, but it'll be useful for a seasoned programmer who finds himself needing to put some effective decision-modeling in his game. Despite a few daunting-looking equations on the cover, the book isn't out to present theory without applications.
Behavioral Mathematics for Game AI isn't a comprehensive guide to AI. It takes on a fairly narrow piece of AI, covers it well, and covers it in a way that it can be applied immediately.