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Task Allocation in Ant Colonies

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Distributed Computing (DISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8784))

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Abstract

In this paper we propose a mathematical model for studying the phenomenon of division of labor in ant colonies. Inside this model we investigate how simple task allocation mechanisms can be used to achieve an optimal division of labor.

We believe the proposed model captures the essential biological features of division of labor in ant colonies and is general enough to study a variety of different task allocation mechanisms. Within this model we propose a distributed randomized algorithm for task allocation that imposes only minimal requirements on the ants; it uses a constant amount of memory and relies solely on a primitive binary feedback function to sense the current labor allocation. We show that with high probability the proposed algorithm converges to a near-optimal division of labor in time which is proportional to the logarithm of the colony size.

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Author information

Authors and Affiliations

  1. School of Engineering and Applied Sciences, Harvard University, USA

    Alejandro Cornejo & Radhika Nagpal

  2. Ecology and Evolutionary Biology, University of Arizona, USA

    Anna Dornhaus

  3. Massachusetts Institute of Technology, CSAIL, USA

    Nancy Lynch

Authors
  1. Alejandro Cornejo
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  2. Anna Dornhaus
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  3. Nancy Lynch
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  4. Radhika Nagpal
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Freiburg, 79110, Freiburg, Germany

    Fabian Kuhn

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© 2014 Springer-Verlag Berlin Heidelberg

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Cornejo, A., Dornhaus, A., Lynch, N., Nagpal, R. (2014). Task Allocation in Ant Colonies. In: Kuhn, F. (eds) Distributed Computing. DISC 2014. Lecture Notes in Computer Science, vol 8784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45174-8_4

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  • DOI: https://doi.org/10.1007/978-3-662-45174-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45173-1

  • Online ISBN: 978-3-662-45174-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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