Recognizing player's activities and hidden state

Wesley Kerr, Paul R. Cohen, Niall Adams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper describes a machine learning approach to classifying the activities of players in games. Instances of activities generally are not identical because they play out in different contexts, so the challenge is to extract the "essences" of activities from instances. We show how this problem may be mapped to a sequence alignment problem, for which there are polynomial-time solutions. The method works well even when some features of activities are not observable (e.g., the emotional states of players). In fact, these features can in some conditions be inferred with high accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th International Conference on the Foundations of Digital Games, FDG 2011
Pages84-90
Number of pages7
DOIs
StatePublished - Dec 1 2011
Event6th International Conference on the Foundations of Digital Games, FDG 2011 - Bordeaux, France
Duration: Jun 29 2011Jul 1 2011

Publication series

NameProceedings of the 6th International Conference on the Foundations of Digital Games, FDG 2011

Other

Other6th International Conference on the Foundations of Digital Games, FDG 2011
Country/TerritoryFrance
CityBordeaux
Period6/29/117/1/11

Keywords

  • Activity classification
  • Hidden state
  • Machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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