Bio-inspired classification in the architecture of situated agents

Abstract

Cognitive development concerns the evolution of human mental capabilities through experience earned during life. Important features needed to accomplish this target are the self-generation of motivations and goals as well as the development of complex behaviors consistent with these goals. Our target is to build such a bio-inspired cognitive architecture for situated agents, capable of integrating new sensing data from any source. Based on neuroscience assessed concepts, as neural plasticity and neural coding, we show how a categorization module built on cascading classifiers is able to interpret different sensing data. Moreover, we see how to give a biological interpretation to our classification model using the winner-take-all paradigm.