Procedural content generation through quality diversity

Daniele Gravina, Ahmed Khalifa, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis

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

    Abstract

    Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.

    Original languageEnglish (US)
    Title of host publicationIEEE Conference on Games 2019, CoG 2019
    PublisherIEEE Computer Society
    ISBN (Electronic)9781728118840
    DOIs
    StatePublished - Aug 2019
    Event2019 IEEE Conference on Games, CoG 2019 - London, United Kingdom
    Duration: Aug 20 2019Aug 23 2019

    Publication series

    NameIEEE Conference on Computatonal Intelligence and Games, CIG
    Volume2019-August
    ISSN (Print)2325-4270
    ISSN (Electronic)2325-4289

    Conference

    Conference2019 IEEE Conference on Games, CoG 2019
    CountryUnited Kingdom
    CityLondon
    Period8/20/198/23/19

    Fingerprint

    Evolutionary algorithms
    Artificial intelligence

    Keywords

    • Evolutionary Computation
    • Expressivity
    • Procedural Content Generation
    • Quality Diversity

    ASJC Scopus subject areas

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

    Cite this

    Gravina, D., Khalifa, A., Liapis, A., Togelius, J., & Yannakakis, G. N. (2019). Procedural content generation through quality diversity. In IEEE Conference on Games 2019, CoG 2019 [8848053] (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/CIG.2019.8848053

    Procedural content generation through quality diversity. / Gravina, Daniele; Khalifa, Ahmed; Liapis, Antonios; Togelius, Julian; Yannakakis, Georgios N.

    IEEE Conference on Games 2019, CoG 2019. IEEE Computer Society, 2019. 8848053 (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2019-August).

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

    Gravina, D, Khalifa, A, Liapis, A, Togelius, J & Yannakakis, GN 2019, Procedural content generation through quality diversity. in IEEE Conference on Games 2019, CoG 2019., 8848053, IEEE Conference on Computatonal Intelligence and Games, CIG, vol. 2019-August, IEEE Computer Society, 2019 IEEE Conference on Games, CoG 2019, London, United Kingdom, 8/20/19. https://doi.org/10.1109/CIG.2019.8848053
    Gravina D, Khalifa A, Liapis A, Togelius J, Yannakakis GN. Procedural content generation through quality diversity. In IEEE Conference on Games 2019, CoG 2019. IEEE Computer Society. 2019. 8848053. (IEEE Conference on Computatonal Intelligence and Games, CIG). https://doi.org/10.1109/CIG.2019.8848053
    Gravina, Daniele ; Khalifa, Ahmed ; Liapis, Antonios ; Togelius, Julian ; Yannakakis, Georgios N. / Procedural content generation through quality diversity. IEEE Conference on Games 2019, CoG 2019. IEEE Computer Society, 2019. (IEEE Conference on Computatonal Intelligence and Games, CIG).
    @inproceedings{813c45c67945424c93290be819cdb8a7,
    title = "Procedural content generation through quality diversity",
    abstract = "Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.",
    keywords = "Evolutionary Computation, Expressivity, Procedural Content Generation, Quality Diversity",
    author = "Daniele Gravina and Ahmed Khalifa and Antonios Liapis and Julian Togelius and Yannakakis, {Georgios N.}",
    year = "2019",
    month = "8",
    doi = "10.1109/CIG.2019.8848053",
    language = "English (US)",
    series = "IEEE Conference on Computatonal Intelligence and Games, CIG",
    publisher = "IEEE Computer Society",
    booktitle = "IEEE Conference on Games 2019, CoG 2019",

    }

    TY - GEN

    T1 - Procedural content generation through quality diversity

    AU - Gravina, Daniele

    AU - Khalifa, Ahmed

    AU - Liapis, Antonios

    AU - Togelius, Julian

    AU - Yannakakis, Georgios N.

    PY - 2019/8

    Y1 - 2019/8

    N2 - Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.

    AB - Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.

    KW - Evolutionary Computation

    KW - Expressivity

    KW - Procedural Content Generation

    KW - Quality Diversity

    UR - http://www.scopus.com/inward/record.url?scp=85073098034&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85073098034&partnerID=8YFLogxK

    U2 - 10.1109/CIG.2019.8848053

    DO - 10.1109/CIG.2019.8848053

    M3 - Conference contribution

    AN - SCOPUS:85073098034

    T3 - IEEE Conference on Computatonal Intelligence and Games, CIG

    BT - IEEE Conference on Games 2019, CoG 2019

    PB - IEEE Computer Society

    ER -