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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2012

01.03.2012 | Swam Intelligence

Analysis of emergent symmetry breaking in collective decision making

verfasst von: Heiko Hamann, Thomas Schmickl, Heinz Wörn, Karl Crailsheim

Erschienen in: Neural Computing and Applications | Ausgabe 2/2012

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Abstract

We investigate a simulated multi-agent system (MAS) that collectively decides to aggregate at an area of high utility. The agents’ control algorithm is based on random agent–agent encounters and is inspired by the aggregation behavior of honeybees. In this article, we define symmetry breaking, several symmetry breaking measures, and report the phenomenon of emergent symmetry breaking within our observed system. The ability of the MAS to successfully break the symmetry depends significantly on a local-neighborhood-based threshold of the agents’ control algorithm that determines at which number of neighbors the agents stop. This dependency is analyzed and two macroscopic features are determined that significantly influence the symmetry breaking behavior. In addition, we investigate the connection between the ability of the MAS to break symmetries and the ability to stay flexible in a dynamic environment.
Vorheriger Artikel Theory and applications of swarm intelligence
Nächster Artikel A correlation-based ant miner for classification rule discovery

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Metadaten
Titel
Analysis of emergent symmetry breaking in collective decision making
verfasst von
Heiko Hamann
Thomas Schmickl
Heinz Wörn
Karl Crailsheim
Publikationsdatum
01.03.2012
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 2/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-010-0368-6

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