Modular self-organization for a long-living autonomous agent
Abstract
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 combine results from two apparently uncorrelated domains: Autonomous planning through Markov Decision Processes and a General Data Clustering Approach using a kernel-like method. Our fundamental idea is that the former is a good framework for addressing autonomy whereas the latter allows to tackle self-organizing problems. Indeed, we derive a modular self-organizing algorithm in which an autonomous agent learns to efficiently spread $n$ planning problems over m initially blank modules.
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