Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks

Andreas Hald, Jens Stuckermann Hansen, Jeppe Theiss Kristensen, Paolo Burelli

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    Abstract

    In this article, we present an experimental approach to using parameterized Generative Adversarial Networks (GANs) to produce levels for the puzzle game Lily’s Garden1. We extract two condition-vectors from the real levels in an effort to control the details of the GAN’s outputs. While the GANs performs well in approximating the first condition (map-shape), they struggle to approximate the second condition (piece distribution). We hypothesize that this might be improved by trying out alternative architectures for both the Generator and Discriminator of the GANs.
    OriginalsprogEngelsk
    TitelFDG '20: International Conference on the Foundations of Digital Games
    ForlagAssociation for Computing Machinery
    Publikationsdato2020
    Artikelnummer99
    ISBN (Elektronisk)9781450388078
    DOI
    StatusUdgivet - 2020
    BegivenhedFDG 2020: Foundations of Digitale Games - Malta, Malta
    Varighed: 16 sep. 202018 sep. 2020
    Konferencens nummer: 2020
    http://fdg2020.org/
    http://fdg2020.org

    Konference

    KonferenceFDG 2020: Foundations of Digitale Games
    Nummer2020
    LokationMalta
    Land/OmrådeMalta
    Periode16/09/202018/09/2020
    Internetadresse

    Emneord

    • Generative Adversarial Networks
    • Puzzle Game Levels
    • Conditional GAN Control
    • Map-Shape Condition
    • Piece Distribution Challenge

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