Unsupervised Modeling of Player Style With LDA

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Gow, Jeremy. 2012. Unsupervised Modeling of Player Style With LDA. IEEE Transactions on Computational Intelligence and AI in Games, 4(3), pp. 152-166. ISSN 1943-068X [Article]

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Abstract or Description

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.

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Article

Identification Number (DOI):

https://doi.org/10.1109/TCIAIG.2012.2213600