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Behavioral Mathematics for Game AI (Applied Mathematics) Paperback – March 5, 2009
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This book is an excellent introduction to using AI in games. Dave has a knack for making complex subjects accessible. The text is very clear and admirably thorough. The author has chosen - wisely - to avoid the esoteric, and focus on topics which are directly useful for making real computer games.-Richard Evans, Senior AI Architect, Electronic Arts
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 also goes into the theoretical background behind these techniques too. For anyone wishing to know more about the maths behind common game behaviors, this is the ideal textbook on the subject.-Alex J. Champandard, Editor & Consultant, AiGameDev.com
About the Author
More About the Author
He is the author of the book "Behavioral Mathematics for Game AI" and is a contributor to the "AI Game Programming Wisdom" and "Game Programming Gems" book series from Charles River Media. Dave is also a founding member of the AI Game Programmers Guild and has spoken at numerous conferences including being a co-adviser for the AI Summits at the annual Game Developers Conference.
Dave continues to further his education by attending the University of Life. He has no plans to graduate any time soon.
Top Customer Reviews
There are only a handful of books on AI for Games, and the techniques used in games are quite a bit different than those used in academic AI. Most of the existing books just look at the standard architectures and topics - behavior trees, state machines, scripting, path planning, etc.
Dave takes a novel approach, and looks at techniques for building heuristics that evaluate the game situation and rate the various options. This is a critical part of decision making, it is something which most games with even moderately complex AI need to do, but it is something that up to now wasn't covered very well (if at all) in the literature.
I heartily recommend this book for anybody who's interested in learning more about how to build decision makers - whether for use in games or elsewhere. The material that is here is not something that you'll find somewhere else.
Dave gives the reader a very strong toolkit for building Game AI, and by toolkit I don't mean a large library of code (although there is plenty of code) but fundamental knowledge, such as the concept of utility and how to apply it in a very practical way. Starting from simple example decisions he builds up to much more complicated cases, constantly tying everything back into practical applications of all the concepts he introduces.
Now, I don't mean to imply that this is some dry math book. Dave constantly sprinkles in his very unique brand of humor and ties in family stories that help cement any topic he's trying to get across. I would highly recommend this book to any professional or aspiring AI Programmer, or even just a player who wants a better understanding of whats going on under the hood in his favorite game.
For starters, I do programming for a living. After 10 years of .Net Web sites and databases, I'm looking to expand my skill set. Not games, necessarily, but AI for sure. I tried a couple math books on Game theory, but they were still a few levels away from practical interpretation. This book brought it down to a level I could use.
In a nutshell, it's about how to program dice for an RPG. The scope is a little broader than that, but this is the main idea. In nontechnical language, the author discusses what the goals of AI are, how rational decisions are made, and how to use probability to generate irrational decisions to model an unpredictable world.
Math wise algebra would be a good idea, and maybe some basic understanding of calculus and statistics, but he gives sufficient background so that your understanding would not be hindered. The book doesn't really teach programming per se, so it really doesn't matter what language you use (although the examples are done in C).Specific algorithms, such was swarming and flocking, are also absent. The material here would be used after you have those ideas down...for example, you have your agents flocking, so now how does the flock decide what to do, and when?
This brings me to the one real draw back to the book. I could tolerate his jokes and stories about his kids, but I suspect some UMLs would have done wonders to make the material clearer. Use cases were almost there in his examples, but it just missed the mark. Maybe this was an attempt to keep the thinking less technical and open it to a wider market, but it is something he should have touched on.Read more ›
If you're looking for good descriptions of challenging topics, this is not the book for you. The diagrams are often confusing (inverting the x-axis for questionable reasons), the code takes up far too much space for the value it is supposed to convey, and the prose is bloated.
The author early on makes the distinction between "proscriptive" and "descriptive" mathematical models - making the point that we're not necessarily trying to find optimal behavior, we're trying to record plausible behavior. This is a reasonable decision, but the amount of work necessary to model the author's favorite example of "4 out of 5 dentists recommending sugar free gum to their patients that chew gum" seems overwrought for what could be accomplished in one line of code.
The author invents a term "response curves", which seems like an awkward name for a selection from a weighted list. He claims that these are necessary, because algorithmic functions don't offer enough flexibility to achieve the results he's looking for. His response curves are piecewise constant functions over a finite domain, and if you want the flexibility of his response curves without the discontinuities, look up splines - well understood, and supported by many libraries already.
The book lurches from dwelling on some easy ideas (scaling the output of a function to be between 0 and 1 and weighted averages) to briefly touching on more complicated ones (marginal utility).Read more ›
Most Recent Customer Reviews
Great book. For modern game AI development this has been very helpful.Published 2 days ago by Joe Petrie
Many books on Game AI feature difficult algorithms and face the challenge of communicating these difficult algorithms to students and practitioners. Read morePublished 21 months ago by Eli
The book paces itself well through the introduction of game theory, and behavior theory, and dives right into well-explained principles and examples. Read morePublished on January 8, 2014 by Bill Merrill
Besides being tediously written, any programmer that's been around for a while will have to groan through pages of elementary interview questions and programming cliches. Read morePublished on December 9, 2013 by D. Ostrowski
As a newcomer to the field of behavioural mathematics and coming from a background that is more visual and artistically oriented, I found this book to be nothing less than the... Read morePublished on December 3, 2013 by lyndon
This is far from what I expected. I halfway expected a half-written book detailing a bunch of mathematics and calculations. Read morePublished on August 3, 2013 by Ernest Mallett
450 pages but most of them are just lengthy explanations of the topic. In the end, you get the idea but you tend to get bored as well. Read morePublished on August 1, 2013 by fafase
If you are a Game Designer or Programmer, you need to read this book, it's incredibly useful. Don't let the word "Mathematics" in the title scare you off, this book is about how to... Read morePublished on April 2, 2013 by Dan
This book introduced me to Dave Mark. Very useful and keep it as a reference for my work. Would recommend it as part of your game programming library.Published on December 5, 2012 by R. Rosini