Indexing by Metric Adaptation and Representation Upgrade in an Emotion-based Agent Model

Following recent neurophysiological research, 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 model is founded on a double knowledge representation paradigm: a stimulus reaching the agent is processed under two different and simultaneous perspectives - a simple (termed perceptual) and a complex (termed cognitive) - from which two differing representation schemata are derived. This paper addresses a twofold strategy for the construction of a perceptual representation. The first one consists in adapting a perceptual metric, with the goal of approximating it to the cognitive metric. The second one has the goal of upgrading the perceptual representation with additional components (e.g., features). Techniques borrowed from nonmetric Multidimensional Scaling are used to approach these goals.