Metric Adaptation and Representation Upgrade in an Emotion-Based Agent Model

Abstract

The research presented here follows a biologically inspired approach, based on the hypothesis that emotions contribute decisively for humans to cope with complex and dynamic environments. This hypothesis is founded on neurophysiological findings showing that damage in the emotion circuitry of the brain cause inability to handle simple, common life tasks [1]. Inspired by these findings, an emotion-based agent model was previously presented [2], proposing a double-processing of stimuli: a simple representation termed perceptual image, designed for fast processing and immediate response to urgent situations, and a complex representation termed cognitive image, thus slow to process, are extracted from each stimulus reaching the agent. These two representations are extracted and processed, simultaneously, by the two levels of the architecture: the perceptual and the cognitive levels. The parallelism of the processing is essential, so that quick response to urgent situations is not compromised by the slow processing of the cognitive level. These two representations are then associated and stored in memory. Once the agent faces a new situation, it matches the incoming stimulus with the agent memory, thus retrieving the associated images.

This work was partially supported by FCT (ISR/IST plurianual funding) through the POS_Conhecimento Program that includes FEDER funds.