Mining rules from player experience and activity data

Jeremy Gow; Simon Colton; Paul Antony Cairns; P Miller

Mining rules from player experience and activity data

Jeremy Gow, Simon Colton, Paul Antony Cairns, P Miller

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Pages	148-153
Publication status	Published - 2012
Event	8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment - Stanford University, Palo Alto, United States Duration: 8 Oct 2012 → 12 Oct 2012

Conference

Conference	8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Country/Territory	United States
City	Palo Alto
Period	8/10/12 → 12/10/12

Access to Document

https://www.aaai.org/ocs/index.php/AIIDE/AIIDE12/paper/view/5460

CAD-GAME: Computer-aided game design
Cairns, P. A., Colton, S. & Gow, J.
EPSRC
1/03/09 → 28/02/12
Project: Research project (funded) › Research

Cite this

@inproceedings{6ad23ae7fb9a43369ac84a76165c4978,

title = "Mining rules from player experience and activity data",

abstract = "Feedback on player experience and behaviour can beinvaluable to game designers, but there is need for specialised knowledge discovery tools to deal with highvolume playtest data. We describe a study with a commercial third-person shooter, in which integrated playeractivity and experience data was captured and mined fordesign-relevant knowledge. Nine dimensions of playerexperience were recorded every few minutes by interrupting play, with a {\textquoteleft}storyboard{\textquoteright} prompt to aid recalland support event-speciﬁc feedback. Association rulelearning was then used to extract rules relating playeractivity and experience during combat, and the resultsﬁltered using four design-relevant rule templates. Theapproach could be used to support the design of gamecontent, including player-adaptive content.",

author = "Jeremy Gow and Simon Colton and Cairns, {Paul Antony} and P Miller",

year = "2012",

language = "English",

pages = "148--153",

booktitle = "8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment",

note = "8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment ; Conference date: 08-10-2012 Through 12-10-2012",

}

Gow, J, Colton, S, Cairns, PA & Miller, P 2012, Mining rules from player experience and activity data. in 8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. pp. 148-153, 8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Palo Alto, United States, 8/10/12. <https://www.aaai.org/ocs/index.php/AIIDE/AIIDE12/paper/view/5460>

TY - GEN

T1 - Mining rules from player experience and activity data

AU - Gow, Jeremy

AU - Colton, Simon

AU - Cairns, Paul Antony

AU - Miller, P

PY - 2012

Y1 - 2012

N2 - Feedback on player experience and behaviour can beinvaluable to game designers, but there is need for specialised knowledge discovery tools to deal with highvolume playtest data. We describe a study with a commercial third-person shooter, in which integrated playeractivity and experience data was captured and mined fordesign-relevant knowledge. Nine dimensions of playerexperience were recorded every few minutes by interrupting play, with a ‘storyboard’ prompt to aid recalland support event-speciﬁc feedback. Association rulelearning was then used to extract rules relating playeractivity and experience during combat, and the resultsﬁltered using four design-relevant rule templates. Theapproach could be used to support the design of gamecontent, including player-adaptive content.

AB - Feedback on player experience and behaviour can beinvaluable to game designers, but there is need for specialised knowledge discovery tools to deal with highvolume playtest data. We describe a study with a commercial third-person shooter, in which integrated playeractivity and experience data was captured and mined fordesign-relevant knowledge. Nine dimensions of playerexperience were recorded every few minutes by interrupting play, with a ‘storyboard’ prompt to aid recalland support event-speciﬁc feedback. Association rulelearning was then used to extract rules relating playeractivity and experience during combat, and the resultsﬁltered using four design-relevant rule templates. Theapproach could be used to support the design of gamecontent, including player-adaptive content.

M3 - Conference contribution

SP - 148

EP - 153

BT - 8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

T2 - 8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

Y2 - 8 October 2012 through 12 October 2012

ER -

Mining rules from player experience and activity data

Abstract

Conference

Access to Document

Projects

CAD-GAME: Computer-aided game design

Cite this