Predicting Player Experience without the Player

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



Publication details

Title of host publicationACM CHI Play 2017
DatePublished - Oct 2017
Original languageEnglish


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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