Player classification using a meta-clustering approach

Daniel Ramirez-Cano, Simon Colton, Robin Baumgarten

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

15 Citations (Scopus)

Abstract

Player classification has recently become a key aspect of game design in areas such as adaptive game systems, player behaviour prediction, player tutoring and non-player character design. Past research has focused on the design of hierarchical, preference-based and probabilistic models aimed at modelling players' behaviour. We propose a meta-classification approach that breaks the clustering of gameplay mixed data into three levels of analysis. The first level uses dimensionality reduction and partitional clustering of aggregate game data in an action/skill-based classification. The second level applies similarity-based clustering of action sequences to group players according to their preferences. For this we propose a new approach which uses Rubner's Earth Mover's Distance (EMD) as a similarity metric to compare histograms of players' game world explorations. The third level applies a combination of social network analysis metrics, such as shortest path length, to social data to find clusters in the players' social network. We test our approach in a gameplay dataset from a freely available first-person social hunting game.

Original languageEnglish
Title of host publicationCGAT 2010 - Computer Games, Multimedia and Allied Technology, Proceedings
Pages297-304
Number of pages8
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event3rd Annual International Conference on Computer Games, Multimedia and Allied Technology, CGAT 2010 - Singapore, Singapore
Duration: 6 Apr 20107 Apr 2010

Publication series

NameCGAT 2010 - Computer Games, Multimedia and Allied Technology, Proceedings

Conference

Conference3rd Annual International Conference on Computer Games, Multimedia and Allied Technology, CGAT 2010
CountrySingapore
CitySingapore
Period6/04/107/04/10

Keywords

  • Clustering
  • Game design
  • Player classification
  • Social gaming

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