Mining rules from player experience and activity data

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

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Title of host publication8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
DatePublished - 2012
Pages148-153
Original languageEnglish

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. Nine dimensions of player
experience were recorded every few minutes by interrupting play, with a ‘storyboard’ prompt to aid recall
and support event-specific feedback. Association rule
learning was then used to extract rules relating player
activity and experience during combat, and the results
filtered using four design-relevant rule templates. The
approach could be used to support the design of game
content, including player-adaptive content.

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