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Modular self-organization for a long-living autonomous agent

Identifieur interne : 000A62 ( Crin/Checkpoint ); précédent : 000A61; suivant : 000A63

Modular self-organization for a long-living autonomous agent

Auteurs : Bruno Scherrer

Source :

RBID : CRIN:scherrer03b

English descriptors

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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CRIN:scherrer03b

Le document en format XML

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<div type="abstract" xml:lang="en" wicri:score="2063">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.</div>
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<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.</abstract>
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