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Randomness
The Statistics of Random Numbers |  |
Topics: General; Genres: General
Abstract: Random numbers are used most heavily by Artificial Intelligence and games in general. To ignore their potential is to make the game predictable and boring. Using them incorrectly can be just as bad as ignoring them outright. Understanding how random numbers are generated, their limitations and their capabilities, can remove many difficulties of using them in your game. This article offers insight into random numbers, their generation, and methods to separate good ones from bad.
Filtered Randomness for AI Decisions and Game Logic | |
Topics: General; Genres: General
Abstract: Conventional wisdom suggests that the better the random number generator, the more unpredictable your game will be. However, according to psychology studies, true randomness over the short term often looks decidedly unrandom to humans. This article shows how to make random AI decisions and game logic look more random to players, while still maintaining strong statistical randomness. Full source code, ready to drop into your game, is supplied on the book's CD-ROM.
Random Map Generation for Strategy Games
Topics: Strategy; Genres: Strategy, RTS
Abstract: While there are numerous articles dedicated to the generation of random maps for games, there is little published information on random maps for strategy games in particular. This subset of map generation presents distinct challenges as evident by the relatively few games that implement them. While the techniques described here can be used to create maps suitable for any type of game, this system is specifically designed to create a variety of successful random maps for real-time strategy games. This article describes the random map generation implementation as found in the RTS game Empire Earth (EE) developed by Stainless Steel Studios.
Imitating Random Variations in Behavior using a Neural Network | |
Topics: Learning, Neural Networks; Genres: General
Abstract: As game AI has increased in sophistication, it has become possible to create computer controlled agents that display remarkably human-like behavior. One of the few indications that an agent is non-organic is the frequently clinical nature of their actions, an effect exacerbated by the often ad hoc mechanisms used to add random variations. This article shows how neural networks can be taught to imitate the actual random variations in behavior that are exhibited by real people. This makes it possible to simulate the playing styles of different sports personalities in unprecedented detail - even the extent to which, for example, the cueing direction and position of the cue ball relative to the cushion affect the accuracy of a pool player's shots. The article assumes minimal knowledge of neural networks and illustrates the techniques through their application to a real game. The CD that accompanies the book contains all the source code for the game, along with that for the neural network class, which is designed as a plug-in component that can easily be transferred to other applications.
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