Strategy-Based Player Modeling during Interactive Entertainment Sessions by Using Bayesian Classification

Strategy-based player modeling is to recognize player's strategy pattern during the gameplay per se. In this paper, Pac-Man game is used as a test-bed. Different Bayesian classifiers like naive Bayes and Bayesian net are chosen to analyze off-line data from gamers who are choosing different str...

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Bibliographic Details
Main Authors: Suoju He, Junping Du, Hongtao Chen, Jin Meng, Qiliang Zhu
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Bayesian classification
Bayesian methods
Computational modeling
Computer science
Data analysis
Helium
Pac-Man
Pattern recognition
Player modeling
Strategy
Telecommunication computing
Testing
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Summary:Strategy-based player modeling is to recognize player's strategy pattern during the gameplay per se. In this paper, Pac-Man game is used as a test-bed. Different Bayesian classifiers like naive Bayes and Bayesian net are chosen to analyze off-line data from gamers who are choosing different strategies, in other words the classifiers are trained with sample data from players using different strategies. The method attempts to use the constructed classifier to predict strategy type of a future player based on the data captured from its gameplay. This paper presents the basic principle of the strategy-based player modeling by using the Bayesian classification theoretic approach and discusses the results of the experiments. The hypothesis proposed in this paper is that Bayesian classification could be used as an approach with excellent performance to recognize player's strategy pattern during real-time game genre.
ISSN:2157-9555
DOI:10.1109/ICNC.2008.68