Using sequential observations to model and predict player behavior

Brent Harrison, David L. Roberts

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

43 Scopus citations

Abstract

In this paper, we present a data-driven technique for designing models of user behavior. Previously, player models were designed using user surveys, small-scale observation experiments, or knowledge engineering. These methods generally produced semantically meaningful models that were limited in their applicability. To address this, we have developed a purely data-driven methodology for generating player models based on past observations of other players. Our underlying assumption is that we can accurately predict what a player will do in a given situation if we examine enough data from former players that were in similar situations. We have chosen to test our method on achievement data from the MMORPG World of Warcraft. Experiments show that our method greatly outperforms a baseline algorithm in both precision and recall, proving that this method can create accurate player models based solely on observation data.

Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on the Foundations of Digital Games, FDG 2011
Pages91-98
Number of pages8
DOIs
StatePublished - 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

Conference

Conference6th International Conference on the Foundations of Digital Games, FDG 2011
Country/TerritoryFrance
CityBordeaux
Period6/29/117/1/11

Keywords

  • Artificial intelligence
  • Design

ASJC Scopus subject areas

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

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