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Behavioral Mathematics for Game AI (Applied Mathematics) Paperback – March 5, 2009

4.4 out of 5 stars 22 customer reviews

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Editorial Reviews

Review

Behavioral Mathematics introduces a raft of important techniques from decision theory, game theory, and utility theory, and uniquely applies them to game AI. These techniques are an important part of any game AI developer's toolbox.-Paul Tozour, Game AI author

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

Dave Mark is the President and Lead Designer of Intrinsic Algorithm, LLC, an independent game development studio and AI consulting company in Omaha, NE. He has been programming since 1984 when he was in high school. Much to the dismay of his teacher, he wrote his first text adventure on the school's DEC PDP-1144. After a brief detour in the music business as a composer/arranger, keyboard player and recording engineer during the early '90s, he re-entered the technology arena in 1995. After being in the IT consulting and development world for 8 years, Dave left to start game, simulation and AI consulting company, Intrinsic Algorithm LLC with his wife, Laurie. He was a contributor to the AI Game Programming Wisdom series and is a regular columnist at AIGameDev.com.
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Product Details

  • Series: Applied Mathematics
  • Paperback: 480 pages
  • Publisher: Cengage Learning PTR; 1st edition (March 5, 2009)
  • Language: English
  • ISBN-10: 1584506849
  • ISBN-13: 978-1584506843
  • Product Dimensions: 7.4 x 1.1 x 9.1 inches
  • Shipping Weight: 2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (22 customer reviews)
  • Amazon Best Sellers Rank: #471,791 in Books (See Top 100 in Books)

More About the Author

Dave is the President and Lead Designer of Intrinsic Algorithm, an independent game development studio and AI consulting company in Omaha, Nebraska. He has been programming since 1985 when he was in high school. (Much to his teacher's dismay, he wrote his first text adventure on the school's DEC PDP-1144 minicomputer.)

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.

Customer Reviews

Top Customer Reviews

Format: Paperback
Truth in advertising: I was the technical editor on this book. However I don't have any further financial interest, so I have no conflict of interest in writing a review. I'm also a professional game developer with 6 titles under my belt, as well as a lecturer on the subject at several prominent universities.

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.
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Format: Paperback
I *love* this book. I simply do not understand the negative comments left by another reviewer. Because of my interest in politics, I want to write code that mimics human behavior, but until Dave Mark's book could not find anything that really "fit." Once upon a time, I took advanced math courses--even excelled in the later ones, but that was nearly 20 years ago, and I haven't really needed that type of math since. Mark explains all the math in easy to understand English. He also provides excel spreadsheets and C++ source that can be downloaded at [...] I don't remember when I've read a book as interesting as _Behavioral Mathematics for Game AI_, and that's saying something since, although I have an advanced degree in computer science, I've never really been a lover of math.
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Format: Paperback
Behavioral Mathematics is a book that I'm sure will continue to prove its worth for many future generations of Game AI Developers. I can make that strong assertion because Dave chose, instead of writing a large tomb about a possible AI architecture, to write a book that tackles (as the title implies) the mathematics behind Game AI.

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.
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Format: Paperback
A lot what I think of a book comes from my expectations. The style, the content and the useful information that it provides me all factor into my opinion.

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.
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Format: Paperback Verified Purchase
The author, at one point, uses a personal anecdote about having agreed with his publisher about how the book should be this many pages, and he was working to fill up that much space. The good news is that the author put words, code, pictures, and diagrams on each of the pages in this book.

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).
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Behavioral Mathematics for Game AI (Applied Mathematics)
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