Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks

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

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

    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.
    Original languageEnglish
    Title of host publicationFDG '20: International Conference on the Foundations of Digital Games
    PublisherAssociation for Computing Machinery
    Publication date2020
    Article number99
    ISBN (Electronic)9781450388078
    DOIs
    Publication statusPublished - 2020
    EventFDG 2020: Foundations of Digitale Games - Malta, Malta
    Duration: 16 Sept 202018 Sept 2020
    Conference number: 2020
    http://fdg2020.org/
    http://fdg2020.org

    Conference

    ConferenceFDG 2020: Foundations of Digitale Games
    Number2020
    LocationMalta
    Country/TerritoryMalta
    Period16/09/202018/09/2020
    Internet address

    Keywords

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

    Fingerprint

    Dive into the research topics of 'Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks'. Together they form a unique fingerprint.

    Cite this