A Survey and Analysis of Techniques for Player Behavior Prediction in Massively Multiplayer Online Role-Playing Games

Brent Harrison, Stephen G. Ware, Matthew W. Fendt, David L. Roberts

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

While there has been much research done on player modeling in single-player games, player modeling in massively multiplayer online role-playing games (MMORPGs) has remained relatively unstudied. In this paper, we survey and evaluate three classes of player modeling techniques: 1) manual tagging; 2) collaborative filtering; and 3) goal recognition. We discuss the strengths and weaknesses that each technique provides in the MMORPG environment using desiderata that outline the traits an algorithm should posses in an MMORPG. We hope that this discussion as well as the desiderata help future research done in this area. We also discuss how each of these classes of techniques could be applied to the MMORPG genre. In order to demonstrate the value of our analysis, we present a case study from our own work that uses a model-based collaborative filtering algorithm to predict achievements in World of Warcraft. We analyze our results in light of the particular challenges faced by MMORPGs and show how our desiderata can be used to evaluate our technique.

Original languageEnglish
Pages (from-to)260-274
Number of pages15
JournalIEEE Transactions on Emerging Topics in Computing
Volume3
Issue number2
DOIs
StatePublished - Jun 1 2015

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Computational Modeling
  • Games
  • Machine Learning

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications

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