PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On adaptive self-organization in artificial robot organisms
Serge Kernbach, Heiko Hamann, Juergen Stradner, Ronald Thenius, Thomas Schmickl, A.C. van Rossum, Michele Sebag, Nicolas Bredeche, Yao Yao, Guy Baele, Yves Van de Peer, John Timmis, Maizura Mohktar, Andrew Tyrrell, A.E. Eiben, S.P. McKibbin, W. Liu and A. F.T. Winfield
In: First IEEE International Conference on Adaptive and Self-adaptive Systems and Applications (IEEE ADAPTIVE 2009), Oct 2009, Greece.

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6786
Deposited By:Nicolas Bredeche
Deposited On:08 March 2010