Towards automatic personalized content generation for platform games

Noor Shaker, Georgios Yannakakis, Julian Togelius

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

    Abstract

    In this paper, we show that personalized levels can be automatically generated for platform games. We build on previous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learning, based on questionnaires administered to players after playing different levels. The contributions of the current paper are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adaptation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010
    Pages63-68
    Number of pages6
    StatePublished - 2010
    Event6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010 - Stanford, CA, United States
    Duration: Oct 11 2010Oct 13 2010

    Publication series

    NameProceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010

    Other

    Other6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010
    CountryUnited States
    CityStanford, CA
    Period10/11/1010/13/10

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Visual Arts and Performing Arts

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