Generalised Player Modelling : Why Artificial Intelligence in Games Should Incorporate Meaning, with a Formalism for so Doing

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dc.contributor.author Cowley, Benjamin Ultan
dc.contributor.editor Fang, Xiaowen
dc.date.accessioned 2020-09-10T13:51:01Z
dc.date.available 2020-09-10T13:51:01Z
dc.date.issued 2020-07-10
dc.identifier.citation Cowley , B U 2020 , Generalised Player Modelling : Why Artificial Intelligence in Games Should Incorporate Meaning, with a Formalism for so Doing . in X Fang (ed.) , HCI in Games : Second International Conference, HCI-Games 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19-24, 2020, Proceedings . Lecture Notes in Computer Science , vol. 12211 , Springer , Cham , pp. 3-22 , International Conference, C &C 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020 , Copenhagen , Denmark , 19/07/2020 . https://doi.org/10.1007/978-3-030-50164-8_1
dc.identifier.citation conference
dc.identifier.other PURE: 140975509
dc.identifier.other PURE UUID: b6dab15a-1380-41fc-b5d0-b1dbfcc2adf8
dc.identifier.other ORCID: /0000-0001-8828-2994/work/80226723
dc.identifier.uri http://hdl.handle.net/10138/319281
dc.description.abstract General game-playing artificial intelligence (AI) has recently seen important advances due to the various techniques known as ‘deep learning’. However, in terms of human-computer interaction, the advances conceal a major limitation: these algorithms do not incorporate any sense of what human players find meaningful in games. I argue that adaptive game AI will be enhanced by a generalised player model, because games are inherently human artefacts which require some encoding of the human perspective in order to respond naturally to individual players. The player model provides constraints on the adaptive AI, which allow it to encode aspects of what human players find meaningful. I propose that a general player model requires parameters for the subjective experience of play, including: player psychology, game structure, and actions of play. I argue that such a player model would enhance efficiency of per-game solutions, and also support study of game-playing by allowing (within-player) comparison between games, or (within-game) comparison between players (human and AI). Here we detail requirements for functional adaptive AI, arguing from first-principles drawn from games research literature, and propose a formal specification for a generalised player model based on our ‘Behavlets’ method for psychologically-derived player modelling. en
dc.format.extent 20
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof HCI in Games
dc.relation.ispartofseries Lecture Notes in Computer Science
dc.relation.isversionof 978-3-030-50163-1
dc.relation.isversionof 978-3-030-50164-8
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 515 Psychology
dc.subject 6162 Cognitive science
dc.subject 113 Computer and information sciences
dc.title Generalised Player Modelling : Why Artificial Intelligence in Games Should Incorporate Meaning, with a Formalism for so Doing en
dc.type Conference contribution
dc.contributor.organization Department of Education
dc.contributor.organization Behavioural Sciences
dc.contributor.organization Cognitive Science
dc.contributor.organization High Performance Cognition group
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1007/978-3-030-50164-8_1
dc.relation.issn 0302-9743
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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