Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning

Vanessa Volz, Niels Justesen, Sam Snodgrass, Sahar Asadi, Sami Purmonen, Christoffer Holmgard, Julian Togelius, Sebastian Risi

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

    Abstract

    Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super Mario Bros., DOOM, Zelda, and Kid Icarus), it is an open question how well these approaches can capture large-scale visual patterns such as symmetry. In this paper, we propose match-three games as a domain to test PCGML algorithms regarding their ability to generate suitable patterns. We demonstrate that popular algorithms such as Generative Adversarial Networks struggle in this domain and propose adaptations to improve their performance. In particular, we augment the neighbourhood of a Markov Random Fields approach to take into account not only local but also symmetric positional information. We conduct several empirical tests, including a user study that show the improvements achieved by the proposed modifications and obtain promising results.

    Original languageEnglish (US)
    Title of host publicationIEEE Conference on Games, CoG 2020
    PublisherIEEE Computer Society
    Pages399-406
    Number of pages8
    ISBN (Electronic)9781728145334
    DOIs
    StatePublished - Aug 2020
    Event2020 IEEE Conference on Games, CoG 2020 - Virtual, Osaka, Japan
    Duration: Aug 24 2020Aug 27 2020

    Publication series

    NameIEEE Conference on Computatonal Intelligence and Games, CIG
    Volume2020-August
    ISSN (Print)2325-4270
    ISSN (Electronic)2325-4289

    Conference

    Conference2020 IEEE Conference on Games, CoG 2020
    CountryJapan
    CityVirtual, Osaka
    Period8/24/208/27/20

    Keywords

    • Candy Crush Saga
    • generative adversarial networks
    • machine learning, global patterns
    • markov random fields
    • procedural content generation

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition
    • Human-Computer Interaction
    • Software

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