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

ABSTRACT

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 data-driven 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 behaviors (spoken language, gesture, posture). Care tracks situational data including locational, intentional, and temporal information to induce a model of empathy. At runtime, CARE uses the model of empathy to drive situation-appropriate empathetic behaviors. CARE has been used in a virtual environment testbed, and an evaluation suggests that the CARE paradigm can provide the basis for effective empathetic behavior control for companion agents.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems

General Terms:
Algorithms, Design, Experimentation, Human Factors

Keywords:
affective reasoning, empathy, synthetic agents