Predicting player behavior in Tomb Raider: Underworld

Tobias Mahlmann, Anders Drachen, Julian Togelius, Alessandro Canossa, Georgios N. Yannakakis

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper presents the results of an explorative study on predicting aspects of playing behavior for the major commercial title Tomb Raider: Underworld (TRU). Various supervised learning algorithms are trained on a large-scale set of in-game player behavior data, to predict when a player will stop playing the TRU game and, if the player completes the game, how long will it take to do so. Results reveal that linear regression models and other nOnlinear classification techniques perform well on the tasks and that decision tree learning induces small yet well-performing and informative trees. Moderate performance is achieved from the prediction models, which indicates the complexity of predicting player behavior based on a constrained set of gameplay metrics and the noise existent in the dataset examined, a generic problem in large-scale data collection from millions of remote clients.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010
    Pages178-185
    Number of pages8
    DOIs
    StatePublished - 2010
    Event2010 IEEE Conference on Computational Intelligence and Games, CIG2010 - Copenhagen, Denmark
    Duration: Aug 18 2010Aug 21 2010

    Publication series

    NameProceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010

    Other

    Other2010 IEEE Conference on Computational Intelligence and Games, CIG2010
    CountryDenmark
    CityCopenhagen
    Period8/18/108/21/10

    Keywords

    • Player modeling
    • Tomb Raider: Underworld
    • classification
    • supervised learning

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Graphics and Computer-Aided Design
    • Software

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  • Cite this

    Mahlmann, T., Drachen, A., Togelius, J., Canossa, A., & Yannakakis, G. N. (2010). Predicting player behavior in Tomb Raider: Underworld. In Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010 (pp. 178-185). [5593355] (Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010). https://doi.org/10.1109/ITW.2010.5593355