A cognitive-affective architecture for ECAs

The development of Embodied Conversational Agents (ECAs) involves a large number of challenges such as the modeling of cognitive and affective functions in order to achieve realism and believability in this type of intelligent agents. An approach to provide ECAs with capabilities for cognitive processing such as learning, decision making, planning, and perception has been the use of cognitive architectures. Moreover, the literature reports several affective models for the generation, classification, and management of emotions in ECAs. Nevertheless, there is a need of cognitive-affective architectures that address the problem of achieving natural interaction and realistic behavior in ECAs. In this paper, we discuss the state of the art on existing cognitive architectures, affective models, and ECAs, and propose a cognitive-affective architecture based on Soar and extended with an affective model inspired by ALMA. The proposed cognitive-affective architecture is designed to allow ECAs to include and take advantage of features such as reinforcement learning, episodic memory, and emotion management.