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JACIII Vol.11 No.3 pp. 319-326
doi: 10.20965/jaciii.2007.p0319
(2007)

Paper:

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Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy

Ruck Thawonmas* and Ji-Young Ho*,**

*Intelligent Computer Entertainment Laboratory, Graduate School of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan

**SAMSUNG ELECTRONICS CO.,LTD.

Received:
April 19, 2006
Accepted:
July 29, 2006
Published:
March 20, 2007
Creative Commons License
Keywords:
player classification, online game, data mining, customer relationship management, game design
Abstract
Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players belong to. In this paper, we propose a new player classification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM). Our approach takes into account both the frequency and order of player action. While HMM performance depends on its structure and initial parameters, our approach requires no parameter settings.
Cite this article as:
R. Thawonmas and J. Ho, “Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.3, pp. 319-326, 2007.
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Last updated on Apr. 19, 2024