University of Hertfordshire

By the same authors

Predicting player experience without the player an exploratory study

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

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Original languageEnglish
Title of host publicationCHI PLAY 2017 - Proceedings of the Annual Symposium on Computer-Human Interaction in Play
PublisherAssociation for Computing Machinery, Inc
Pages305-315
Number of pages11
ISBN (Electronic)9781450348980
DOIs
Publication statusPublished - 15 Oct 2017
Event4th ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, CHI PLAY 2017 - Amsterdam, Netherlands
Duration: 15 Oct 201718 Oct 2017

Conference

Conference4th ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, CHI PLAY 2017
CountryNetherlands
CityAmsterdam
Period15/10/1718/10/17

Abstract

A key challenge of procedural content generation (PCG) is to evoke a certain player experience (PX), when we have no direct control over the content which gives rise to that experience. We argue that neither the rigorous methods to assess PX in HCI, nor specialised methods in PCG are sufficient, because they rely on a human in the loop. We propose to address this shortcoming by means of computational models of intrinsic motivation and AI game-playing agents. We hypothesise that our approach could be used to automatically predict PX across games and content types without relying on a human player or designer. We conduct an exploratory study in level generation based on empowerment, a specific model of intrinsic motivation. Based on a thematic analysis, we find that empowerment can be used to create levels with qualitatively different PX.We relate the identified experiences to established theories of PX in HCI and game design, and discuss next steps.

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