C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Inferring user goals from personality and behavior in a causal model of user affect
Proceedings of the 8th international conference on Intelligent user interfaces
What makes things fun to learn? heuristics for designing instructional computer games
SIGSMALL '80 Proceedings of the 3rd ACM SIGSMALL symposium and the first SIGPC symposium on Small systems
Machine Learning
Spatial Presence and Emotions during Video Game Playing: Does It Matter with Whom You Play?
Presence: Teleoperators and Virtual Environments
Explaining Winning Poker--A Data Mining Approach
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
21st Century Game Design (Game Development Series)
21st Century Game Design (Game Development Series)
Rules of Play: Game Design Fundamentals
Rules of Play: Game Design Fundamentals
A new approach to classification based on association rule mining
Decision Support Systems
Enhancing E-Learning Engagement Using Design Patterns from Computer Games
ACHI '08 Proceedings of the First International Conference on Advances in Computer-Human Interaction
Can feature information interaction help for information fusion in multimedia problems?
Multimedia Tools and Applications
Machine learning in digital games: a survey
Artificial Intelligence Review
Player modeling using self-organization in tomb raider: underworld
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Experience Assessment and Design in the Analysis of Gameplay
Simulation and Gaming
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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.