Crowdsourcing the aesthetics of platform games

Noor Shaker, Georgios N. Yannakakis, Julian Togelius

    Research output: Contribution to journalArticlepeer-review


    What are the aesthetics of platform games and what makes a platform level engaging, challenging, and/or frustrating We attempt to answer such questions through mining a large set of crowdsourced gameplay data of a clone of the classic platform game Super Mario Bros (SMB). The data consist of 40 short game levels that differ along six key level design parameters. Collectively, these levels are played 1560 times over the Internet, and the perceived experience is annotated by experiment participants via self-reported ranking (pairwise preferences). Given the wealth of this crowdsourced data, as all details about players' in-game behavior are logged, the problem becomes one of extracting meaningful numerical features at the appropriate level of abstraction for the construction of generic computational models of player experience and, thereby, game aesthetics. We explore dissimilar types of features, including direct measurements of event and item frequencies, and features constructed through frequent sequence mining, and go through an in-depth analysis of the interrelationship between level content, players' behavioral patterns, and reported experience. Furthermore, the fusion of the extracted features allows us to predict reported player experience with a high accuracy, even from short game segments. In addition to advancing our insight on the factors that contribute to platform game aesthetics, the results are useful for the personalization of game experience via automatic game adaptation.

    Original languageEnglish (US)
    Article number6373713
    Pages (from-to)276-290
    Number of pages15
    JournalIEEE Transactions on Computational Intelligence and AI in Games
    Issue number3
    StatePublished - 2013


    • Computational aesthetics
    • experience-driven procedural content generation
    • player experience modeling

    ASJC Scopus subject areas

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


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