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I Feel I Feel You: A Theory of Mind Experiment in Games

dc.contributor.authorMelhart, David
dc.contributor.authorYannakakis, Georgios N.
dc.contributor.authorLiapis, Antonios
dc.date.accessioned2021-04-23T09:30:27Z
dc.date.available2021-04-23T09:30:27Z
dc.date.issued2020
dc.description.abstractIn this study into the player’s emotional theory of mind (ToM) of gameplaying agents, we investigate how an agent’s behaviour and the player’s own performance and emotions shape the recognition of a frustrated behaviour. We focus on the perception of frustration as it is a prevalent affective experience in human-computer interaction. We present a testbed game tailored towards this end, in which a player competes against an agent with a frustration model based on theory. We collect gameplay data, an annotated ground truth about the player’s appraisal of the agent’s frustration, and apply face recognition to estimate the player’s emotional state. We examine the collected data through correlation analysis and predictive machine learning models, and find that the player’s observable emotions are not correlated highly with the perceived frustration of the agent. This suggests that our subject’s ToM is a cognitive process based on the gameplay context. Our predictive models—using ranking support vector machines—corroborate these results, yielding moderately accurate predictors of players’ ToM.de
dc.identifier.doi10.1007/s13218-020-00641-2
dc.identifier.pissn1610-1987
dc.identifier.urihttp://dx.doi.org/10.1007/s13218-020-00641-2
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36272
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 34, No. 1
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectAffective computing
dc.subjectArtificial agents
dc.subjectDigital games
dc.subjectPreference learning
dc.subjectTheory of mind
dc.titleI Feel I Feel You: A Theory of Mind Experiment in Gamesde
dc.typeText/Journal Article
gi.citation.endPage55
gi.citation.startPage45

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