User Modeling and User-Adapted Interaction

, Volume 23, Issue 5, pp 489–526

Real-time rule-based classification of player types in computer games


    • Cognitive Science Unit, Department of Behavioural ScienceUniversity of Helsinki
    • Center for Knowledge and Innovation ResearchAalto Business School, Aalto University
  • Darryl Charles
    • School of Computing and Information EngineeringUniversity of Ulster
  • Michaela Black
    • School of Computing and Information EngineeringUniversity of Ulster
  • Ray Hickey
    • School of Computing and Information EngineeringUniversity of Ulster
Original Paper

DOI: 10.1007/s11257-012-9126-z

Cite this article as:
Cowley, B., Charles, D., Black, M. et al. User Model User-Adap Inter (2013) 23: 489. doi:10.1007/s11257-012-9126-z


The power of using machine learning to improve or investigate the experience of play is only beginning to be realised. For instance, the experience of play is a psychological phenomenon, yet common psychological concepts such as the typology of temperaments have not been widely utilised in game design or research. An effective player typology provides a model by which we can analyse player behaviour. We present a real-time classifier of player type, implemented in the test-bed game Pac-Man. Decision Tree algorithms CART and C5.0 were trained on labels from the DGD player typology (Bateman and Boon, 21st century game design, vol. 1, 2005). The classifier is then built by selecting rules from the Decision Trees using a rule- performance metric, and experimentally validated. We achieve ~70% accuracy in this validation testing. We further analyse the concept descriptions learned by the Decision Trees. The algorithm output is examined with respect to a set of hypotheses on player behaviour. A set of open questions is then posed against the test data obtained from validation testing, to illustrate the further insights possible from extended analysis.


Player typologyPlayer profilingComputer gamesDecision treesClassificationExperimental validation

Supplementary material

11257_2012_9126_MOESM1_ESM.doc (43 kb)
ESM 1 (DOC 43 kb)

Copyright information

© Springer Science+Business Media B.V. 2012