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Simply put, AI for Games and Animation: A Cognitive Modeling Approach shows how video game characters can be taught to "think" for themselves. This groundbreaking title points the way for the future of intelligent gaming. While this book features formal mathematical proofs of its arguments, it also provides a worthwhile overview of how today's video game characters are currently programmed, as well as future possibilities for making them "smarter" (and more realistic) through techniques borrowed from AI and robotics. At this level, the text shows off the possibility of adding cognitive models (expressed in the author's own Cognitive Modeling Language, CML) to the games of the near future. While today's games feature behavior models and incorporate the rules of physics to create realistic scenes, this book makes a strong case that by adding intelligence to characters, game realism can be enhanced further while actually simplifying development. Though this book is certainly worthy as an academic treatise on the subject, the author keeps an eye on real-world programming issues. For instance, he suggests that adding AI to game characters will coexist with current programming techniques. He is also careful to pay attention to the limits of CPU and graphics processing power when suggesting new algorithms. The most abstract sections here show off the mathematical techniques (and modeling language) for adding knowledge representation, sensing, and learning to characters. Later sections offering two simulated worlds (featuring T-Rexes and raptors, for example) show off the author's argument to good advantage. Though it requires a background in math to appreciate fully, AI for Games and Animation can be read profitably by anyone interested in the future of gaming. It is only a matter of time before games adopt some of the techniques presented here, and serious game developers will get a head start with this intriguing and groundbreaking book. --Richard Dragan Topics covered: Cognitive modeling for games and simulations, geometric and behavior models, domain knowledge, sensing, interval arithmetic basics, creating nondeterministic behavior, Cognitive Modeling Language (CML), and sample model worlds. |
