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ATAL
2006
Springer

Learning empathy: a data-driven framework for modeling empathetic companion agents

14 years 3 months ago
Learning empathy: a data-driven framework for modeling empathetic companion agents
Affective reasoning plays an increasingly important role in cognitive accounts of social interaction. Humans continuously assess one another's situational context, modify their own affective state accordingly, and then respond to these outcomes by expressing empathetic behaviors. Synthetic agents serving as companions should respond similarly. However, empathetic reasoning is riddled with the complexities stemming from the myriad factors bearing upon situational assessment. A key challenge posed by affective reasoning in synthetic agents is devising empirically informed models of empathy that accurately respond in social situations. This paper presents CARE, a datadriven affective architecture and methodology for learning models of empathy by observing human-human social interactions. First, in CARE training sessions, one trainer directs synthetic agents to perform a sequence of tasks while another trainer manipulates companion agents' affective states to produce empathetic ...
Scott W. McQuiggan, James C. Lester
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where ATAL
Authors Scott W. McQuiggan, James C. Lester
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