Intentional computational level design

Ahmed Khalifa, Gabriella Barros, Michael Cerny Green, Julian Togelius

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

    Abstract

    The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives maximized. In this work, we address the problem of creating levels that are not only playable but also revolve around specific mechanics in the game. We use constrained evolutionary algorithms and quality-diversity algorithms to generate small sections of Super Mario Bros levels called scenes, using three different simulation approaches: Limited Agents, Punishing Model, and Mechanics Dimensions. All three approaches are able to create scenes that give opportunity for a player to encounter or use targeted mechanics with different properties. We conclude by discussing the advantages and disadvantages of each approach and compare them to each other.

    Original languageEnglish (US)
    Title of host publicationGECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference
    PublisherAssociation for Computing Machinery, Inc
    Pages796-803
    Number of pages8
    ISBN (Electronic)9781450361118
    DOIs
    StatePublished - Jul 13 2019
    Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
    Duration: Jul 13 2019Jul 17 2019

    Publication series

    NameGECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference

    Conference

    Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
    CountryCzech Republic
    CityPrague
    Period7/13/197/17/19

    Fingerprint

    Mechanics
    Video Games
    Evolutionary algorithms
    Evolutionary Algorithms
    Game
    Design
    Simulation
    Model

    Keywords

    • Constrained Map-Elites
    • Feasible Infeasible 2-Pop
    • Genetic Algorithms
    • Level Design
    • Procedural Content Generation

    ASJC Scopus subject areas

    • Computational Mathematics

    Cite this

    Khalifa, A., Barros, G., Green, M. C., & Togelius, J. (2019). Intentional computational level design. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 796-803). (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3321707.3321849

    Intentional computational level design. / Khalifa, Ahmed; Barros, Gabriella; Green, Michael Cerny; Togelius, Julian.

    GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, 2019. p. 796-803 (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference).

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

    Khalifa, A, Barros, G, Green, MC & Togelius, J 2019, Intentional computational level design. in GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, pp. 796-803, 2019 Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 7/13/19. https://doi.org/10.1145/3321707.3321849
    Khalifa A, Barros G, Green MC, Togelius J. Intentional computational level design. In GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc. 2019. p. 796-803. (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/3321707.3321849
    Khalifa, Ahmed ; Barros, Gabriella ; Green, Michael Cerny ; Togelius, Julian. / Intentional computational level design. GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, 2019. pp. 796-803 (GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference).
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