Adaptive Artificial Intelligence in Games

Issues, Requirements, and a Solution through Behavlets-based General Player Modelling

Benjamin Ultan Cowley, Darryl Charles

    Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinen

    Kuvaus

    We present the last of a series of three academic essays which deal with the question of how and why to build a generalized player model. We propose that a general player model needs parameters for subjective experience of play, including: player psychology, game structure, and actions of play. Based on this proposition, we pose three linked research questions: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence- based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? We set out the arguments behind these research questions in each of the three essays, presented as three preprints. The third essay, in this preprint, presents the argument that adaptive game artificial intelligence will be enhanced by a generalised player model. This is because games are inherently human artefacts which therefore, require some encoding of the human perspective in order to effectively autonomously respond to the individual player. The player model informs the necessary constraints on the adaptive artificial intelligence. A generalised player model is not only more efficient than a per-game solution, but also allows comparison between games which makes it a useful tool for studying play in general. We describe the concept and meaning of an adaptive game. We propose requirements for functional adaptive AI, arguing from first principles drawn from the games research literature. We propose solutions to these requirements, based on a formal model approach to our existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.
    Alkuperäiskielienglanti
    ArtikkeliarXiv:1607.05082
    LehtiarXiv.org
    Sivumäärä8
    ISSN2331-8422
    TilaJulkaistu - 18 heinäkuuta 2016
    OKM-julkaisutyyppiB1 Kirjoitus tieteellisessä aikakauslehdessä

    Lisätietoja

    8 pages, 1 figure
    Volume:
    Proceeding volume:

    Tieteenalat

    • 515 Psykologia

    Lainaa tätä

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    title = "Adaptive Artificial Intelligence in Games: Issues, Requirements, and a Solution through Behavlets-based General Player Modelling",
    abstract = "We present the last of a series of three academic essays which deal with the question of how and why to build a generalized player model. We propose that a general player model needs parameters for subjective experience of play, including: player psychology, game structure, and actions of play. Based on this proposition, we pose three linked research questions: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence- based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? We set out the arguments behind these research questions in each of the three essays, presented as three preprints. The third essay, in this preprint, presents the argument that adaptive game artificial intelligence will be enhanced by a generalised player model. This is because games are inherently human artefacts which therefore, require some encoding of the human perspective in order to effectively autonomously respond to the individual player. The player model informs the necessary constraints on the adaptive artificial intelligence. A generalised player model is not only more efficient than a per-game solution, but also allows comparison between games which makes it a useful tool for studying play in general. We describe the concept and meaning of an adaptive game. We propose requirements for functional adaptive AI, arguing from first principles drawn from the games research literature. We propose solutions to these requirements, based on a formal model approach to our existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.",
    keywords = "cs.HC, 515 Psychology",
    author = "Cowley, {Benjamin Ultan} and Darryl Charles",
    note = "8 pages, 1 figure Volume: Proceeding volume:",
    year = "2016",
    month = "7",
    day = "18",
    language = "English",
    journal = "arXiv.org",
    issn = "2331-8422",
    publisher = "Cornell University",

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    Adaptive Artificial Intelligence in Games : Issues, Requirements, and a Solution through Behavlets-based General Player Modelling. / Cowley, Benjamin Ultan; Charles, Darryl.

    julkaisussa: arXiv.org , 18.07.2016.

    Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinen

    TY - JOUR

    T1 - Adaptive Artificial Intelligence in Games

    T2 - Issues, Requirements, and a Solution through Behavlets-based General Player Modelling

    AU - Cowley, Benjamin Ultan

    AU - Charles, Darryl

    N1 - 8 pages, 1 figure Volume: Proceeding volume:

    PY - 2016/7/18

    Y1 - 2016/7/18

    N2 - We present the last of a series of three academic essays which deal with the question of how and why to build a generalized player model. We propose that a general player model needs parameters for subjective experience of play, including: player psychology, game structure, and actions of play. Based on this proposition, we pose three linked research questions: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence- based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? We set out the arguments behind these research questions in each of the three essays, presented as three preprints. The third essay, in this preprint, presents the argument that adaptive game artificial intelligence will be enhanced by a generalised player model. This is because games are inherently human artefacts which therefore, require some encoding of the human perspective in order to effectively autonomously respond to the individual player. The player model informs the necessary constraints on the adaptive artificial intelligence. A generalised player model is not only more efficient than a per-game solution, but also allows comparison between games which makes it a useful tool for studying play in general. We describe the concept and meaning of an adaptive game. We propose requirements for functional adaptive AI, arguing from first principles drawn from the games research literature. We propose solutions to these requirements, based on a formal model approach to our existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.

    AB - We present the last of a series of three academic essays which deal with the question of how and why to build a generalized player model. We propose that a general player model needs parameters for subjective experience of play, including: player psychology, game structure, and actions of play. Based on this proposition, we pose three linked research questions: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence- based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? We set out the arguments behind these research questions in each of the three essays, presented as three preprints. The third essay, in this preprint, presents the argument that adaptive game artificial intelligence will be enhanced by a generalised player model. This is because games are inherently human artefacts which therefore, require some encoding of the human perspective in order to effectively autonomously respond to the individual player. The player model informs the necessary constraints on the adaptive artificial intelligence. A generalised player model is not only more efficient than a per-game solution, but also allows comparison between games which makes it a useful tool for studying play in general. We describe the concept and meaning of an adaptive game. We propose requirements for functional adaptive AI, arguing from first principles drawn from the games research literature. We propose solutions to these requirements, based on a formal model approach to our existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.

    KW - cs.HC

    KW - 515 Psychology

    M3 - Article

    JO - arXiv.org

    JF - arXiv.org

    SN - 2331-8422

    M1 - arXiv:1607.05082

    ER -