Mining Rules from Player Experience and Activity Data


  • Jeremy Gow Imperial College London
  • Simon Colton Imperial College London
  • Paul Cairns University of York
  • Paul Miller Rebellion Developments Ltd



Association rule learning, player modelling, player experience


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 witha 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 extractmeaningful 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.




How to Cite

Gow, J., Colton, S., Cairns, P., & Miller, P. (2021). Mining Rules from Player Experience and Activity Data. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 8(1), 148-153.