Optimization of platform game levels for player experience

Chris Pedersen, Julian Togelius, Georgios Yannakakis

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

    Abstract

    We demonstrate an approach to modelling the effects of certain parameters of platform game levels on the players' experience of the game. A version of Super Mario Bros has been adapted for generation of parameterized levels, and experiments are conducted over the web to collect data on the relationship between level design parameters and aspects of player experience. These relationships have been learned using preference learning of neural networks. The acquired models will form the basis for artificial evolution of game levels that elicit desired player emotions.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 5th Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009
    Pages191-192
    Number of pages2
    StatePublished - Dec 1 2009
    Event5th Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009 - Stanford, CA, United States
    Duration: Oct 14 2009Oct 16 2009

    Publication series

    NameProceedings of the 5th Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009

    Other

    Other5th Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009
    CountryUnited States
    CityStanford, CA
    Period10/14/0910/16/09

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Visual Arts and Performing Arts

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  • Cite this

    Pedersen, C., Togelius, J., & Yannakakis, G. (2009). Optimization of platform game levels for player experience. In Proceedings of the 5th Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009 (pp. 191-192). (Proceedings of the 5th Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009).