计算机游戏中的可计算专家知识

K. Fujii, F. Hsieh, Cho-Jui Hsieh
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引用次数: 0

摘要

我们通过算法计算和演示计算机游戏的多尺度专家知识,通过在两个异质性水平上的模式组合。分层聚类(HC)用于构建基于块的热图:由行轴和列轴上施加的两个分层树构成的彩色矩阵。计算出的异质性可以诱导出与不同地图集群相关的可行游戏功能的不同集合。在游戏层面上,地图依赖的异质性揭示了哪些游戏特征模式构成确实具有近乎确定性的获胜或失败,哪些对应于50% - 50%的结果不确定性。因此,这种模式组合成为游戏前预测和游戏中策略的关键知识基础。电脑游戏TagPro: Capture The Flag,在整个论文的开发过程中被用作一个说明例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computable Expert Knowledge in Computer Games
We algorithmically compute and demonstrate multi-scale expert knowledge of computer gaming through pattern compositions on two levels of heterogeneity. Hierarchical clustering (HC) is applied to construct block-based heatmaps: colored matrices framed by two hierarchical trees imposed upon row and column axes. The computed heterogeneity is seen to induce different collections of viable gaming features pertaining to different map-clusters. On the game level, the map-dependent heterogeneity is seen to reveal which gaming-feature-pattern compositions are indeed viable for wins or losses with near-certainty, and which correspond to 50-50 uncertainty in outcome. Hence, such pattern compositions become the critical knowledge bases for pre-game prediction as well as ongoing-gaming strategy. The computer game, TagPro: Capture the Flag, is used as an illustrating example throughout the development of this paper.
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