Responding efficiently to relevant stimuli using an emotion-based agent architecture

It is widely accepted that one important role of emotions consists in providing a mechanism for adequate and efficient response to relevant stimuli. In this paper we propose a methodology for implementing such a mechanism, based on a previously presented emotion-based agent model. This agent model is biologically inspired in the emotion mechanisms found in the brain, following recent neurophysiological research. This model is founded on two principles: (1) stimuli is represented internally by two representations with different degrees of complexity and accuracy, and (2) the matching of these representations is implemented by a distance function. The mechanism considered in this paper amounts to matching the current stimulus the agent is perceiving with its past experience. This paper addresses a twofold strategy for optimizing the efficiency and accuracy of this mechanism. The first one consists in adapting the distance function employed in one of the representations, while the second one has the goal of upgrading that representation with new relevant features. Techniques borrowed from nonmetric multidimensional scaling are used to approach these goals.