Unsupervised Modeling of Player Style With LDA
Unsupervised Modeling of Player Style With LDA
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DOI：
10.1109/tciaig.2012.2213600
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发表时间：
2012-09-01
影响因子：
--
通讯作者：
Miller, Paul
中科院分区：
文献类型：
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作者：
Gow, Jeremy;Baumgarten, Robin;Miller, Paul
玩家风格的计算分析对于视频游戏设计具有巨大的潜力：它可以提供对玩家行为的洞察，以及动态调整游戏以适应每个人的游戏风格的方法。为了实现这一潜力，计算方法需要超越对挑战和能力的考虑，并考虑玩家风格的美学方面。我们在这里描述了一种使用多类线性判别分析（LDA）来建模玩家风格的半自动无监督学习方法。我们认为这种方法广泛适用于对各种游戏（包括商业应用）中的玩家风格进行建模，并通过两个案例研究来说明：第一个是名为 Snakeotron 的新颖街机游戏，第二个是现代商业第三人称射击视频游戏 Rogue Trooper。
Computational analysis of player style has significant potential for video game design: it can provide insights into player behavior, as well as the means to dynamically adapt a game to each individual's style of play. To realize this potential, computational methods need to go beyond considerations of challenge and ability and account for aesthetic aspects of player style. We describe here a semiautomatic unsupervised learning approach to modeling player style using multiclass linear discriminant analysis (LDA). We argue that this approach is widely applicable for modeling player style in a wide range of games, including commercial applications, and illustrate it with two case studies: the first for a novel arcade game called Snakeotron, and the second for Rogue Trooper, a modern commercial third-person shooter video game.