Improving situated agents adaptability using interruption theory of emotions

Emotions play several important roles in the cognition of human beings and other life forms, and are therefore a legitimate inspiration to provide adaptability and autonomy to situated agents. However, there is no unified theory of emotions and many discoveries are yet to be made in the applicability of emotions to situated agents. This paper investigates the feasibility and utility of an artificial model of anger and fear based on Interruption Theory of Emotions. This model detects and highlights situations for which an agent's decision-making mechanism is no longer pertinent. These situations are detected by analyzing discrepancies between the agent's actions and its intentions, making this model independent from the agent's environment and tasks. Collective foraging simulations are used to characterize the influence of the model. Results show that the model improves the adaptability of a group of agents by simultaneously optimizing multiple performance criterion.