Combining Causal and Diagnostic Assessment in a Probabilistic Model of User Affect

By Heather Maclaren


We present our most recent work in the development of a probabilistic model of user affect, which is designed to allow an intelligent agent to recognise multiple user emotions within an uncontrolled environment. Our model deals with the high level of uncertainty involved in this task by combining information on both the causes and effects of emotional reactions within a Dynamic Decision Network. In previous work we designed the causal part of the model by relying on empirical data integrated with relevant psychological theories of emotion and personality. The focus of this talk will be on our work devoted to understanding if and how some of the studentís emotional assessment could be more easily provided by the part of the model that diagnoses emotional states from their observable effects.

This is joint work with Cristina Conati.

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