Aggregation of action symbol sub-sequences for discovery of online-game player characteristics using keygraph

Authors:
Ruck Thawonmas;Katsuyoshi Hata
Affiliations:
Intelligent Computer Entertainment Laboratory, Department of Human and Computer Intelligence, Ritsumeikan University, Shiga, Japan;Intelligent Computer Entertainment Laboratory, Department of Human and Computer Intelligence, Ritsumeikan University, Shiga, Japan
Venue:
ICEC'05 Proceedings of the 4th international conference on Entertainment Computing
Year:
2005

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Abstract

Keygraph is a visualization tool for discovery of relations among text-based data. This paper discusses a new application of KeyGraph for discovery of player characteristics in Massively Multiplayer Online Games (MMOGs). To achieve high visualization ability for this application, we propose a preprocessing method that aggregates action symbol sub-sequences of players into more informative forms. To verify whether this aim is achieved, we conduct an experiment where human subjects are asked to classify types of players in a simulated MMOG with KeyGraphs using and not using the proposed preprocessing method. Experimental results confirm the effectiveness of the proposed method.