A general theoretical framework for the design of artificial emotion systems in Autonomous Agents

Autonomous Agents (AAs) capable of exhibiting emotional behaviors have contributed to the development of natural human-machine interactions in several application domains. In order to provide AAs with emotional mechanisms, their underlying architecture must implement an Artificial Emotion System (AES), a computational model that imitates specific facets of human emotions. Although several AES have been reported in related literature, their design is generally supported on several emotion theories, leading researchers to model and integrate isolated emotion components and mechanisms into the architectures of AES. This theoretical foundation of AES contributes to ambiguities in the analysis and comparison of their underlying architectures, which demands the definition of standards, design guidelines, and integrative frameworks. In this paper, we present a psychologically inspired theoretical framework designed to serve as a platform for the unification of AES' components, the comparison of AES, and the design and implementation of AES in AAs. We analyze common emotion-related requirements of AES, emotion components involved in the design of this type of computational model, and emotion theories that drive the design of most AES. The validation of this framework demonstrates its compatibility with current AES and its feasibility as a model for unifying multiple emotional theories.