Mining rules from player experience and activity data

Jeremy Gow, Simon Colton, Paul Cairns, Paul Miller

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

4 Citations (Scopus)

Abstract

Feedback on player experience and behaviour can be invaluable to game designers, but there is need for specialised knowledge discovery tools to deal with high volume playtest data. We describe a study with a commercial third-person shooter, in which integrated player activity and experience data was captured and mined for design-relevant knowledge. We demonstrate that association rule learning and rule templates can be used to extract meaningful rules relating player activity and experience during combat. We found that the number, type and quality of rules varies between experiences, and is affected by feature distributions. Further work is required on rule selection and evaluation.

Original languageEnglish
Title of host publicationProceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012
Pages148-153
Number of pages6
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012 - Stanford, United States of America
Duration: 8 Oct 201212 Oct 2012

Publication series

NameProceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012

Conference

Conference8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012
CountryUnited States of America
CityStanford
Period8/10/1212/10/12

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