On adaptive self-organization in artificial robot organisms

Serge Kernbach Heiko Hamann Juergen Stradtner Ronald Thenius Thomas Schmickl Karl Crailsheim Anne Van Rossum Michèle Sebag 1 Nicolas Bredeche 1, 2, 3 Yao Yao 4 Guy Baele Yves Van de Peer John Timmis Maizura Mohktar Andy Tyrell A.E. Eiben S.P. Mckibbin Wenguo Liu 5 Alan Winfield
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
2 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France, Polytechnique - X, CNRS - Centre National de la Recherche Scientifique : UMR7161
Abstract : In Nature, self-organization demonstrates very reliable and scalable collective behavior in a distributed fashion. In collective robotic systems, self-organization makes it possible to address both the problem of adaptation to quickly changing environment and compliance with user-defined target objectives. This paper describes on-going work on artificial self-organization within artificial robot organisms, performed in the framework of the Symbrion and Replicator European projects.
Type de document :
Communication dans un congrès
IEEE Adaptive, 2009, Athens, Greece. 2009
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https://hal.inria.fr/inria-00413281
Contributeur : Nicolas Bredeche <>
Soumis le : jeudi 3 septembre 2009 - 16:24:21
Dernière modification le : jeudi 11 janvier 2018 - 17:22:02

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  • HAL Id : inria-00413281, version 1

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Serge Kernbach, Heiko Hamann, Juergen Stradtner, Ronald Thenius, Thomas Schmickl, et al.. On adaptive self-organization in artificial robot organisms. IEEE Adaptive, 2009, Athens, Greece. 2009. 〈inria-00413281〉

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