Unsupervised modeling of player style with LDA

Jeremy Gow, Robin Baumgarten, Paul Cairns, Simon Colton, Paul Miller

Research output: Contribution to journalArticleResearchpeer-review

25 Citations (Scopus)

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.

Original languageEnglish
Article number6269992
Pages (from-to)152-166
Number of pages15
JournalIEEE Transactions on Computational Intelligence and AI in Games
Volume4
Issue number3
DOIs
Publication statusPublished - 24 Sep 2012
Externally publishedYes

Keywords

  • Adaptive games
  • k-means clustering
  • linear discriminant analysis (LDA)
  • log analysis
  • player style
  • player types
  • video games

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