A Hybrid Symbolic-Connectionist Approach to Modeling Emotions

Randolph M. Jones, Eric Chown, and Amy E. Henninger

This paper describes a framework for modeling emotions in an interactive, decision-making agent. In tune with modern theories of emotions (e.g., Damasio, 1995; LeDoux, 1992), we regard emotions essentially as subconscious signals and evaluations that inform, modify, and receive feedback from a variety of sources including higher cognitive processes and the sensorimotor system. Thus, our work explicitly distinguishes the subconscious processes (in a connectionist implementation) and the decision making that is subject to emotional influences (in a symbolic cognitive architecture). Because our project focuses upon decision making, it emphasizes aspects of emotion that influence higher cognition and not those that affect, for example, the immune system. We are integrating a connectionist model of emotions from Chown (1993) with Rosenbloom, Laird, and Newell’s (1993; Newell, 1990) Soar architecture. Sponsored by the Army, the application area incorporates emotions and individual differences into behavior models of synthetic virtual pilots in a battlefield simulation. Intelligent agents in this application area must exercise a variety of reasoning capabilities, including situation assessment, planning, reacting to goal failures, and interacting with a team of agents. Although we are developing the framework for this model within the military domain, we intend the framework to generalize across interactive agents. Figure 1 provides a sketch of the integrated architecture. In our framework, symbolic assessments of a small set of "emotional attributes" reside in a working memory, which serves as the interface between deliberative cognitive processes and the emotion mechanisms. Working memory elements combine with background knowledge to generate strategies, reasoning, and external behavior, as well as working interpretations of the environment and status of internal goals (situational awareness). Some of these interpretations and assessments feed into the connectionist model, which in turn continuously computes new values for each emotional attribute. This paper presents a work in progress. We have begun implementation of the architecture on top of an existing Soar model, but have not yet begun testing.


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