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From the same journal

Unsupervised modelling of player style with LDA

Research output: Contribution to journalArticle

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Unsupervised modelling of player style with LDA. / Gow, Jeremy; Baumgarten, Robin; Cairns, Paul Antony; Colton, Simon; Miller, P.

In: Computational Intelligence and AI in Games, IEEE Transactions on, Vol. 4, No. 3, 6269992, 09.2012, p. 152-166.

Research output: Contribution to journalArticle

Harvard

Gow, J, Baumgarten, R, Cairns, PA, Colton, S & Miller, P 2012, 'Unsupervised modelling of player style with LDA', Computational Intelligence and AI in Games, IEEE Transactions on, vol. 4, no. 3, 6269992, pp. 152-166. https://doi.org/10.1109/TCIAIG.2012.2213600

APA

Gow, J., Baumgarten, R., Cairns, P. A., Colton, S., & Miller, P. (2012). Unsupervised modelling of player style with LDA. Computational Intelligence and AI in Games, IEEE Transactions on, 4(3), 152-166. [6269992]. https://doi.org/10.1109/TCIAIG.2012.2213600

Vancouver

Gow J, Baumgarten R, Cairns PA, Colton S, Miller P. Unsupervised modelling of player style with LDA. Computational Intelligence and AI in Games, IEEE Transactions on. 2012 Sep;4(3):152-166. 6269992. https://doi.org/10.1109/TCIAIG.2012.2213600

Author

Gow, Jeremy ; Baumgarten, Robin ; Cairns, Paul Antony ; Colton, Simon ; Miller, P. / Unsupervised modelling of player style with LDA. In: Computational Intelligence and AI in Games, IEEE Transactions on. 2012 ; Vol. 4, No. 3. pp. 152-166.

Bibtex - Download

@article{c6378e801089497e82761b700c133f43,
title = "Unsupervised modelling of player style with LDA",
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.",
keywords = "Video games, adaptive games, player style, player types, log analysis, LDA, k-means clustering",
author = "Jeremy Gow and Robin Baumgarten and Cairns, {Paul Antony} and Simon Colton and P Miller",
year = "2012",
month = "9",
doi = "10.1109/TCIAIG.2012.2213600",
language = "English",
volume = "4",
pages = "152--166",
journal = "Computational Intelligence and AI in Games, IEEE Transactions on",
issn = "1943-068X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Unsupervised modelling of player style with LDA

AU - Gow, Jeremy

AU - Baumgarten, Robin

AU - Cairns, Paul Antony

AU - Colton, Simon

AU - Miller, P

PY - 2012/9

Y1 - 2012/9

N2 - 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.

AB - 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.

KW - Video games

KW - adaptive games

KW - player style

KW - player types

KW - log analysis

KW - LDA

KW - k-means clustering

U2 - 10.1109/TCIAIG.2012.2213600

DO - 10.1109/TCIAIG.2012.2213600

M3 - Article

VL - 4

SP - 152

EP - 166

JO - Computational Intelligence and AI in Games, IEEE Transactions on

JF - Computational Intelligence and AI in Games, IEEE Transactions on

SN - 1943-068X

IS - 3

M1 - 6269992

ER -