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

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 desi...

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Bibliographic Details
Published in:User modeling and user-adapted interaction 2013-11, Vol.23 (5), p.489-526
Main Authors: Cowley, Ben, Charles, Darryl, Black, Michaela, Hickey, Ray
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Classification
Classifiers
Computer & video games
Computer Science
Decision trees
Descriptions
Design
Design engineering
Electronic & video games
Hypotheses
Investigations
Machine learning
Management of Computing and Information Systems
Multimedia Information Systems
Original Paper
Personality
Players
Real time
Studies
User Interfaces and Human Computer Interaction
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Summary: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.
ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-012-9126-z