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The contributors--there are dozens--share their professional experience in the field of simulating life in software. Some of their examples are crystal-clear--Nick Porcino's block diagrams that explain the behavior of artificial insects are one example. What's more, the compilation is remarkably comprehensive, addressing such issues as how collectives (such as platoons of soldiers) exhibit behavior that takes into account the perceptions of their members (as well as the lack of information--the fog of war). This book is carefully designed to help game architects plan more exciting and realistic environments for their players. --David Wall
Topics covered: How to design a computer game in which computer-managed characters need to behave intelligently. There's coverage of how to encode a character's motivations, how to make a character relate to a group, how to make characters move through and perceive space, and how to make a character learn over time.
AI Game Programming Wisdom 2 is structured very much the same as the first book in the series. The articles are broken into similar sections, with the addition of a new section on finite-state-machines and splitting the section on learning into two. Having already covered the A* algorithm, its various optimizations, and navigation meshes in the previous books those topics are virtually absent here (though they may be buried as a secondary topic in some of the path-finding articles). I thought that the series had exhausted discussion of finite-state machines, but the editors managed to include a couple of interesting articles. The demand for more realistic AI behavior in character-based games in growing. And while a full simulation of emotions and human characteristics is unteneable, at the moment, there are some interesting ideas in the Learning section of the book e.g. "Motivational Graphs: A New Architecture for Complex Behavior Simulation.".
I can't say that this book is indispensible from an implementation perspective. Most of the articles are simply too vague - though there are notable exceptions throughout the entire series of books. What I found most useful in these articles was that they stimulated thought and helped to generate discussion among the programmers and designers at work.
Ok, let us not try to get bogged down into semantics. The book does have many nice, state of the art (2004) methods for handling diverse problems arising in game coding. For example, the section on speech recognition is quite well done. The main thing to take away from that section is that you need to restrict the problem so that the range of possible user responses is limited. Which may not necessarily be what that author intended. But from your point of view of easier and more robust coding, this is the pragmatic way to use speech recognition.
Of course, an AI purist might argue that what I have just suggested is not really AI. I agree. I use the speech recognition as just one example. You may derive the greatest benefit from this book if you deprecate the AI aspect and just see what methods you can usefully cull. Always remember that true AI is really hard. You are writing games. NOT research. Leave that to others.
Reality must take precedence over public relations, for Mother Nature
cannot be fooled.
-- R.P. Feynman
Q: Why did the WASP cross the road?
A: To get to the middle.