A Fuzzy Logic Computational Model for Emotion Regulation Based on Gross

Emotion regulation looks into methods and strategies that humans use in order to control and balance their possible extreme levels of emotions. One important challenge in building a computational model of emotions is the mainly non-quantitative nature of this problem. In this paper, we investigate a Fuzzy logic approach as a possible framework for providing the required qualitative and quantitative description of such models. In our proposed fuzzy computational model which was constructed based on Gross theory for emotion regulation, beside the fuzzy structure, it includes a learning module that enhances the model adaptivity to environmental changes through learning some relevant aspects such as patterns of events’ sequences. The results of the simulation experiments were compared against a formerly presented non-fuzzy implementation. We observed that the agents in our proposed model managed to cope better with changes in the environment and exhibited smoother regulation behavior. Moreover, our model showed further consistency with the inferential rules of Gross theory.