Talakat: Bullet hell generation through constrained map-elites

Ahmed Khalifa, Andy Nealen, Scott Lee, Julian Togelius

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

    Abstract

    We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles. Levels are represented using a domain-specific description language, and search in the space defined by this language is performed by a novel variant of the Map-Elites algorithm which incorporates a feasible-infeasible approach to constraint satisfaction. Simulation-based evaluation is used to gauge the fitness of levels, using an agent based on best-first search. The performance of the agent can be tuned according to the two dimensions of strategy and dexterity, making it possible to search for level configurations that require a specific combination of both. As far as we know, this paper describes the first generator for this game genre, and includes several algorithmic innovations.

    Original languageEnglish (US)
    Title of host publicationGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
    PublisherAssociation for Computing Machinery, Inc
    Pages1047-1054
    Number of pages8
    ISBN (Electronic)9781450356183
    DOIs
    StatePublished - Jul 2 2018
    Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
    Duration: Jul 15 2018Jul 19 2018

    Publication series

    NameGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference

    Other

    Other2018 Genetic and Evolutionary Computation Conference, GECCO 2018
    CountryJapan
    CityKyoto
    Period7/15/187/19/18

    Keywords

    • Bullet Hell
    • Constraint Map-Elites
    • Description Language
    • Framework

    ASJC Scopus subject areas

    • Computer Science Applications
    • Software
    • Computational Theory and Mathematics

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