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 language | English |
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Title of host publication | Proceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012 |
Pages | 148-153 |
Number of pages | 6 |
Publication status | Published - 1 Dec 2012 |
Externally published | Yes |
Event | AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 2012 - Stanford, United States of America Duration: 8 Oct 2012 → 12 Oct 2012 Conference number: 8th http://aiide12.gatech.edu/ https://www.aaai.org/Library/AIIDE/aiide12contents.php (Proceedings) |
Publication series
Name | Proceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012 |
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Conference
Conference | AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 2012 |
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Abbreviated title | AIIDE 2012 |
Country/Territory | United States of America |
City | Stanford |
Period | 8/10/12 → 12/10/12 |
Internet address |