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User Type Identification in Virtual Worlds

  • Ruck Thawonmas
  • Ji-Young Ho
  • Yoshitaka Matsumoto
Chapter
Part of the Agent-Based Social Systems book series (ABSS, volume 2)

Conclusions

In this chapter we have presented an efiective approach for identification of user types in virtual worlds. Two types of input features were discussed, action-based features and item-based features. The former type uses the information on the frequency of each type of action that each user performed. The latter one uses the information on the frequency of each type of item that each user acquired. AMBR, adopted as the classifier, could successfully identify the type of unknown user agents. In addition, it could give higher performance with the item-based features. In future work, we plan to conduct experiments using agents with more complicated behaviors and to investigate use of order information in either action sequences or item sequences. Eventually, we will apply our findings to real user data.

Keywords

Recognition Rate Virtual World Input Feature User Type User Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Tokyo 2005

Authors and Affiliations

  • Ruck Thawonmas
    • 1
  • Ji-Young Ho
    • 1
  • Yoshitaka Matsumoto
    • 1
  1. 1.Intelligent Computer Entertainment Laboratory, Department of Human and Computer ScienceRitsumeikan UniversityKusatsu, ShigaJapan

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