Modeling player experience in Super Mario Bros

Chris Pedersen, Julian Togelius, Georgios N. Yannakakis

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


    This paper investigates the relationship between level design parameters of platform games, individual playing characteristics and player experience. The investigated design parameters relate to the placement and sizes of gaps in the level and the existence of direction changes; components of player experience include fun, frustration and challenge. A neural network model that maps between level design parameters, playing behavior characteristics and player reported emotions is trained using evolutionary preference learning and data from 480 platform game sessions. Results show that challenge and frustration can be predicted with a high accuracy (77.77% and 88.66% respectively) via a simple single-neuron model whereas model accuracy for fun (69.18%) suggests the use of more complex non-linear approximators for this emotion. The paper concludes with a discussion on how the obtained models can be utilized to automatically generate game levels which will enhance player experience.

    Original languageEnglish (US)
    Title of host publicationCIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games
    Number of pages8
    StatePublished - 2009
    EventCIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games - Milano, Italy
    Duration: Sep 7 2009Sep 10 2009

    Publication series

    NameCIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games


    OtherCIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games


    • Content creation
    • Entertainment modeling
    • Fun
    • Neuroevolution
    • Platform games
    • Player satisfaction
    • Preference learning

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computational Theory and Mathematics
    • Computer Graphics and Computer-Aided Design
    • Human-Computer Interaction


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