Artificial Intelligence for Games, Second Edition by Ian Millington, John Funge

By Ian Millington, John Funge

The majority of software program improvement books, even if or not it's for line-of-business app dev or online game improvement, appear to have little to no details that may be came across through an off-the-cuff net search.

This booklet is likely one of the few exceptions. there's a clean breadth and intensity of online game AI wisdom during this publication that has been of large aid. in contrast to the typical "Gems" sequence of books, this booklet comprises sufficient info on approximately each subject for the reader to construct a 'ground up' implementation in their own.

My purely court cases are that the pseudocode appears to be like overly simplified and never as simply switched over to a concrete implementation as i would like, and that even for a booklet on game-specific AI implementations, the authors appear to get pleasure from a section extra of an academic/idealized method of the layout. that would be much less bothersome to a pro online game developer, yet i am on the hobbyist/indie point, and infrequently desire a quick-and-dirty implementation sooner than I start to fairly comprehend what is going on.

Having acknowledged that, i used to be in a position to use the publication to profit approximately and enforce goal-oriented motion making plans, quickly and versatile A* course discovering (with more information on transformed funnel set of rules online), and several serious parts.

I might completely suggest this ebook.

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Extra info for Artificial Intelligence for Games, Second Edition

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Free from the necessity for any natural interpretation, the probablistic framework could be extended to found the core of modern statistical AI that includes Bayes nets, support-vector machines (SVMs), and Gaussian processes. Engineering The sea change in academic AI is more than a fashion preference. It has made AI a key technology that is relevant to solving real-world problems. Google’s search technology, for example, is underpinned by this new approach to AI. It is no coincidence that Peter Norvig is both Google’s Director of Research and the co-author (along with his former graduate advisor, professor Stuart Russell) of the canonical reference for modern academic AI [Russell and Norvig, 2002].

Instead, you start with a design for a character and apply the most relevant tool to get the result. This means that what qualifies as game AI may be unrecognizable as an AI technique. In the previous chapter, we looked at the AI for Pac-Man ghosts—a simple random number generator applied judiciously. Generating a random number isn’t an AI technique as such. In most languages there are built-in functions to get a random number, so there is certainly no point giving an algorithm for it! But it can work in a surprising number of situations.

This means that what qualifies as game AI may be unrecognizable as an AI technique. In the previous chapter, we looked at the AI for Pac-Man ghosts—a simple random number generator applied judiciously. Generating a random number isn’t an AI technique as such. In most languages there are built-in functions to get a random number, so there is certainly no point giving an algorithm for it! But it can work in a surprising number of situations. , 2000]. While there are reasonably complicated things going on under the surface, a lot of the character behavior is communicated with animation.

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