Issue No. 03 - Sept. (2012 vol. 4)
Jeremy Gow , Computational Creativity Group, Department of Computing, Imperial College London, London, U.K.
Robin Baumgarten , Computational Creativity Group, Department of Computing, Imperial College London, London, U.K.
Paul Cairns , Human Computer Interaction Research Group, Department of Computer Science, University of York, Heslington, York, U.K.
Simon Colton , Computational Creativity Group, Department of Computing, Imperial College London, London, U.K.
Paul Miller , Rebellion Developments Ltd., Oxford, U.K.
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.
computer games, social aspects of automation, unsupervised learning
J. Gow, R. Baumgarten, P. Cairns, S. Colton and P. Miller, "Unsupervised modeling of player style with LDA," in IEEE Transactions on Computational Intelligence and AI in Games, vol. 4, no. 3, pp. 152-166, 2012.