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Unsupervised modelling of player style with LDA

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JournalComputational Intelligence and AI in Games, IEEE Transactions on
DateE-pub ahead of print - 16 Aug 2012
DatePublished (current) - Sep 2012
Issue number3
Volume4
Number of pages15
Pages (from-to)152-166
Early online date16/08/12
Original languageEnglish

Abstract

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.

    Research areas

  • Video games, adaptive games, player style, player types, log analysis, LDA, k-means clustering

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