Feature Analysis for Modeling Game Content Quality

Noor Shaker, Georgios N. Yannakakis, Julian Togelius

    Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


    entertainment for individual game players is to tailor player
    experience in real-time via automatic game content generation.
    Modeling the relationship between game content and player
    preferences or affective states is an important step towards
    this type of game personalization. In this paper we analyse the
    relationship between level design parameters of platform games
    and player experience. We introduce a method to extract the
    most useful information about game content from short game
    sessions by investigating the size of game session that yields
    the highest accuracy in predicting players’ preferences, and by
    defining the smallest game session size for which the model
    can still predict reported emotion with acceptable accuracy.
    Neuroevolutionary preference learning is used to approximate
    the function from game content to reported emotional preferences.
    The experiments are based on a modified version
    of the classic Super Mario Bros game. We investigate two
    types of features extracted from game levels; statistical level
    design parameters and extracted frequent sequences of level
    elements. Results indicate that decreasing the size of the feature
    window lowers prediction accuracy, and that the models built
    on selected features derived from the whole set of extracted
    features (combining the two types of features) outperforms
    other models constructed on partial information about game
    Original languageEnglish
    Title of host publicationCIG 2011. Proceedings of the IEEE conference on computational intelligence and games
    Number of pages8
    PublisherIEEE Computer Society Press
    Publication date2011
    ISBN (Electronic)978-1-4577-0009-5
    Publication statusPublished - 2011


    • player experience
    • real-time game personalization
    • level design parameters
    • neuroevolutionary preference learning
    • prediction accuracy


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