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Learning from House-Hunting Ants: Collective Decision-Making in Organic Computing Systems

  • Arne Brutschy
  • Alexander Scheidler
  • Daniel Merkle
  • Martin Middendorf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)

Abstract

This paper proposes ant-inspired strategies for self-organized and decentralized collective decision-making in computing systems which employ reconfigurable units. The particular principles used for the design of these strategies are inspired by the house-hunting of the ant Temnothorax albipennis. The considered computing system consists of two types of units: so-called worker units that are able to execute jobs that come into the system, and scout units that are additionally responsible for the reconfiguration process of all units. The ant-inspired strategies are analyzed experimentally and are compared to a non-adaptive reference strategy. It is shown that the ant-inspired strategies lead to a collective decentralized decision process through which the units are able to find good configurations that lead to a high system throughput even in complex configuration spaces.

Keywords

Nest Site Task Allocation Work Unit Supplementary Online Material Adaptive Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Arne Brutschy
    • 1
  • Alexander Scheidler
    • 2
  • Daniel Merkle
    • 3
  • Martin Middendorf
    • 2
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  2. 2.Parallel Computing and Complex Systems Group, Computer Science DepartmentUniversity of LeipzigLeipzigGermany
  3. 3.Department of Mathematics and Computer ScienceUniversity of Southern DenmarkOdenseDenmark

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