Modeling player experience for content creation

Christopher Pedersen, Julian Togelius, Georgios N. Yannakakis

    Research output: Contribution to journalArticlepeer-review


    In this paper, we use computational intelligence techniques to built quantitative models of player experience for a platform game. The models accurately predict certain key affective states of the player based on both gameplay metrics that relate to the actions performed by the player in the game, and on parameters of the level that was played. For the experiments presented here, a version of the classic Super Mario Bros game is enhanced with parameterizable level generation and gameplay metrics collection. Player pairwise preference data is collected using forced choice questionnaires, and the models are trained using this data and neuroevolutionary preference learning of multilayer perceptrons (MLPs). The derived models will be used to optimize design parameters for particular types of player experience, allowing the designer to automatically generate unique levels that induce the desired experience for the player.

    Original languageEnglish (US)
    Article number5420018
    Pages (from-to)54-67
    Number of pages14
    JournalIEEE Transactions on Computational Intelligence and AI in Games
    Issue number1
    StatePublished - Mar 2010


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

    ASJC Scopus subject areas

    • Software
    • Control and Systems Engineering
    • Artificial Intelligence
    • Electrical and Electronic Engineering


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