Modular self-organization for a long-living autonomous agent

The aim of this paper is to provide a sound framework for addressing a difficult problem: the automatic construction of an autonomous agent’s modular architecture. We briefly present two apparently uncorrelated frameworks: Autonomous planning through Markov Decision Processes and Kernel Clustering. Our fundamental idea is that the former addresses autonomy whereas the latter allows to tackle self-organizing issues. Relying on both frameworks, we show that modular selforganization can be formalized as a clustering problem in the space of MDPs. We derive a modular self-organizing algorithm in which an autonomous agent learns to efficiently spread n planning problems over m initially blank modules with m < n.