Top > JACIII > Classification of Online Game Players Using Action Transition ...

single-jc.php

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

Paper:

Views over last 60 days: 520

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.
Data files:
References
  1. [1] K. Alexander, R. Batle, E. Castronova, G. Costikyan, J. Hayter, T. Kurz, D. Manachi, and J. Smith, The Themis Report 2004 – Preview, 2004.
  2. [2] R. Thawonmas, J. Y. Ho, and Y. Matsumoto, “User Type Identification in Virtual Worlds,” Agent-Based Modeling Meets Gaming Simulation (Post-Proceedings of the Session Conference of the ISAGA, International Simulation and Gaming Association, 2003), Series: Springer Series on Agent Based Social Systems, Vol.2, K. Arai, H. Deguchi, and H. Matsui (Eds.), Springer, pp. 79-88, March, 2006.
  3. [3] L. Shi and W. Huang, “Apply Social Network Analysis and Data Mining to Dynamic Task Synthesis for Persistent MMORPG Virtual World,” Lecture Notes in Computer Science, M. Rauterberg (Ed.), Vol.3166 (Proc. ICEC 2004), pp. 204-215, Sep., 2004.
  4. [4] Y. Matsumoto and R. Thawonmas, “MMOG Player Classification Using Hidden Markov Models,” Lecture Notes in Computer Science, M. Rauterberg (Ed.), Vol.3166 (Proc. ICEC 2004), pp. 429-434, Sep., 2004.
  5. [5] L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc. IEEE, Vol.77(2), pp. 257-285, Feb., 1989.
  6. [6] G. Deco and D. Obradovic, “An Information-Theoretic Approach to Neural Computing,” Springer, Berlin, Germany, 1996.
  7. [7] R. Bartle, “Hearts, Clubs, Diamonds, Spades: Players Who Suit MUDs,” The Journal of Virtual Environments, 1(1), 1996,
  8. [8] A. Tveit, Y. Rein, V. I. Jorgen, and M. Matskin, “Scalable Agent-Based Simulation of Players in Massively Multiplayer Online Games,” Proc. the 8th Scandinavian Conference on Artificial Intelligence (SCAI2003), Bergen, Norway, Nov., 2003.
  9. [9] J. Y. Ho and R. Thawonmas, “Episode Detection with Vector Space Model in Agent Behavior Sequences of MMOGs,” Proc. Future Business Technology Conference 2004 (FUBUTEC’2004), INSEAD, Fontainebleau, France, pp. 47-54, Mar., 2004.
  10. [10] J. A. B. Michael and G. Linoff, “Data Mining Techniques: For Marketing, Sales, and Customer Support,” John Wiley & Sons, Inc., N.Y., 1997.
  11. [11] http://www.ice.ci.ritsumei.ac.jp/˜ruck/downloads.html

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 01, 2024