Generating levels that teach mechanics

Michael Cerny Green, Ahmed Khalifa, Gabriella A.B. Barros, Andy Nealen, Julian Togelius

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

    Abstract

    The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A∗agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 13th International Conference on the Foundations of Digital Games, FDG 2018
    EditorsSebastian Deterding, Mitu Khandaker, Sebastian Risi, Jose Font, Steve Dahlskog, Christoph Salge, Carl Magnus Olsson
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9781450365710
    DOIs
    StatePublished - Aug 7 2018
    Event13th International Conference on the Foundations of Digital Games, FDG 2018 - Malmo, Sweden
    Duration: Aug 7 2018Aug 10 2018

    Publication series

    NameACM International Conference Proceeding Series

    Other

    Other13th International Conference on the Foundations of Digital Games, FDG 2018
    Country/TerritorySweden
    CityMalmo
    Period8/7/188/10/18

    Keywords

    • Feasible infeasible 2-population
    • Search based level generation
    • Super mario bros

    ASJC Scopus subject areas

    • Software
    • Human-Computer Interaction
    • Computer Vision and Pattern Recognition
    • Computer Networks and Communications

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