  In this study into the player's emotional theory of mind 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 theory of mind 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' theory of
mind.

Artificial Intelligence;Human-Computer Interaction;Machine Learning