Download AI Game Programming Wisdom (Game Development Series) by Steve Rabin PDF

By Steve Rabin

Find out how AI specialists create clever video game items and characters with this primary quantity within the AI online game Programming knowledge sequence. This distinct choice of articles provides programmers and builders entry to the insights and knowledge of over thirty AI professionals. each one article delves deep into key AI online game programming matters and offers insightful new principles and strategies that may be simply built-in into your personal video games. every little thing from basic AI architectures, rule dependent platforms, level-of-detail AI, scripting language concerns, to professional structures, fuzzy common sense, neural networks, and genetic algorithms are completely coated. if you are a online game programmer (AI/logic, front-end, person interface, instruments, pics, etc.) this finished source may also help you're taking your talents and data to the subsequent point.

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Extra info for AI Game Programming Wisdom (Game Development Series)

Example text

This implies some kind of communication AI—an area understood quite well, but which is often less successfully implemented. We might understand the challenges of conversational AI, and we definitely understand why it is so difficult to achieve, but we are unable to solve it. Were this an area of AI that was solved, the Turing Test (and the competition that goes with it) would already have been passed and the competition won. We do, however, have a collection of sophisticated models to deploy that ought to work reasonably well within the context of a game.

Whether this has an actual application in a shooter, for example, is up to the game developer; it may not be worth the effort or may not have an application. In a video game, let us assume that the AI is capable of analyzing a new state from an existing state and can give it a score. The score attributed to the state, from the game’s point of view, is optimized toward the player’s failure; the worse off the player becomes, the better it is for the game (opposition). When using alpha-beta pruning, the trick in the minimax algorithm is that the AI also takes into account the opposition’s moves (in this case the player), as well as its own.

It is, though, possible to layer different levels of A-Life granularity (grain) as well as allow them to occupy the same game space. Some facets of the game universe will be based on coarse granularity, while others will have fine granularity. A good example of fine-grain A-Life is, of course, Creatures. Almost every aspect of the creature is modeled finely, but the environment is modeled at a coarser level. The balls and blocks that are used as toys are not modeled with the same granularity; they do not need to be.

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