Empirically building and evaluating a probabilistic model of user affect |
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Authors: | Cristina Conati Heather Maclaren |
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Affiliation: | (1) Department of Computer Science, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada;(2) Humanature Studios, Nexon Publishing North America, Vancouver, Canada |
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Abstract: | We present a probabilistic model of user affect designed to allow an intelligent agent to recognise multiple user emotions
during the interaction with an educational computer game. Our model is based on a probabilistic framework that deals with
the high level of uncertainty involved in recognizing a variety of user emotions by combining in a Dynamic Bayesian Network
information on both the causes and effects of emotional reactions. The part of the framework that reasons from causes to emotions
(diagnostic model) implements a theoretical model of affect, the OCC model, which accounts for how emotions are caused by one’s appraisal of
the current context in terms of one’s goals and preferences. The advantage of using the OCC model is that it provides an affective
agent with explicit information not only on which emotions a user feels but also why, thus increasing the agent’s capability to effectively respond to the users’ emotions. The challenge is that building the
model requires having mechanisms to assess user goals and how the environment fits them, a form of plan recognition. In this paper, we illustrate how we built the predictive part of the affective model by combining general theories with
empirical studies to adapt the theories to our target application domain. We then present results on the model’s accuracy,
showing that the model achieves good accuracy on several of the target emotions. We also discuss the model’s limitations,
to open the ground for the next stage of the work, i.e., complementing the model with diagnostic information.
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Keywords: | Affective computing Dynamic Bayesian networks Evaluation User modeling |
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