GAIG Game AI Research Group @ QMUL

Intrinsic Motivation in Computational Creativity Applied to Videogames


Abstract

Computational creativity (CC) seeks to endow artificial systems with creativity. Although human creativity is known to be substantially driven by intrinsic motivation (IM), most CC systems are extrinsically motivated. This restricts their actual and perceived creativity and autonomy, and consequently their benefit to people. In this thesis, we demonstrate, via theoretical arguments and through applications in videogame AI, that computational intrinsic reward and models of IM can advance core CC goals. We introduce a definition of IM to contextualise related work. Via two systematic reviews, we develop typologies of the benefits and applications of intrinsic reward and IM models in CC and game AI. Our reviews highlight that related work is limited to few reward types and motivations, and we thus investigate the usage of empowerment, a little studied, information-theoreticintrinsic reward, in two novel models applied to game AI. We define coupled empowerment maximisation (CEM), a social IM model, to enable general co-creative agents that support or challenge their partner through emergent behaviours. Via two qualitative, observational vignette studies on a custom-made videogame, we explore CEM’s ability to drive general and believable companion and adversary non-player characters which respond creatively to changes in their abilities and the game world. We moreover propose to leverage intrinsic reward to estimate people’s experience of interactive artefacts in an autonomous fashion. We instantiate this proposal in empowerment-based player experience prediction (EBPXP) and apply it to videogame procedural content generation. By analysing thinkaloud data from an experiential vignette study on a dedicated game, we identify several experiences that EBPXP could predict. Our typologies serve as inspiration and reference for CC and game AI researchers to harness the benefits of IM in their work. Our new models can increase the generality, autonomy and creativity of next-generation videogame AI, and of CC systems in other domains.

Cite this work

@phdthesis{guckelsberger2020phd,
author= {Guckelsberger, Christian},
title= {{Intrinsic Motivation in Computational Creativity Applied to Videogames}},
year= {2020},
school= {Queen Mary University of London, UK},
abstract= {Computational creativity (CC) seeks to endow artificial systems with creativity. Although human creativity is known to be substantially driven by intrinsic motivation (IM), most CC systems are extrinsically motivated. This restricts their actual and perceived creativity and autonomy, and consequently their benefit to people. In this thesis, we demonstrate, via theoretical arguments and through applications in videogame AI, that computational intrinsic reward and models of IM can advance core CC goals. We introduce a definition of IM to contextualise related work. Via two systematic reviews, we develop typologies of the benefits and applications of intrinsic reward and IM models in CC and game AI. Our reviews highlight that related work is limited to few reward types and motivations, and we thus investigate the usage of empowerment, a little studied, information-theoreticintrinsic reward, in two novel models applied to game AI. We define coupled empowerment maximisation (CEM), a social IM model, to enable general co-creative agents that support or challenge their partner through emergent behaviours. Via two qualitative, observational vignette studies on a custom-made videogame, we explore CEM’s ability to drive general and believable companion and adversary non-player characters which respond creatively to changes in their abilities and the game world. We moreover propose to leverage intrinsic reward to estimate people’s experience of interactive artefacts in an autonomous fashion. We instantiate this proposal in empowerment-based player experience prediction (EBPXP) and apply it to videogame procedural content generation. By analysing thinkaloud data from an experiential vignette study on a dedicated game, we identify several experiences that EBPXP could predict. Our typologies serve as inspiration and reference for CC and game AI researchers to harness the benefits of IM in their work. Our new models can increase the generality, autonomy and creativity of next-generation videogame AI, and of CC systems in other domains.},
}

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