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Ant-Inspired Dynamic Task Allocation via Gossiping

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Stabilization, Safety, and Security of Distributed Systems (SSS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10616))

Abstract

We study the distributed task allocation problem in multi-agent systems, where each agent selects a task in such a way that, collectively, they achieve a proper global task allocation. In this paper, inspired by specialization on division of labor in ant colonies, we propose several scalable and efficient algorithms to dynamically allocate the agents as the task demands change. The algorithms have their own pros and cons, with respect to (1) how fast they react to dynamic demands change, (2) how many agents need to switch tasks, (3) whether extra agents are needed, and (4) whether they are resilient to faults.

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Notes

  1. 1.

    Although the assumption that every agent knows all the demands seems to be strong, as long as each demand is known by some agent, all the demands can be propagated to everyone in \(O(\log n )\) rounds by gossiping (see broadcasting in Sect. 2).

  2. 2.

    With high probability, which means with probability at least \(1 - 1/{\text {poly}}(n)\). Note that if there are \({\text {poly}}(n)\) events and each one holds w.h.p., then all of them simultaneously hold w.h.p. by an union bound argument.

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Authors and Affiliations

  1. CSAIL, MIT, Cambridge, USA

    Hsin-Hao Su, Lili Su & Nancy Lynch

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

    Anna Dornhaus

Authors
  1. Hsin-Hao Su
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  2. Lili Su
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  3. Anna Dornhaus
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  4. Nancy Lynch
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Correspondence to Hsin-Hao Su .

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Editors and Affiliations

  1. University of Liverpool, Liverpool, United Kingdom

    Paul Spirakis

  2. Chalmers University of Technology, Gothenburg, Sweden

    Philippas Tsigas

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Su, HH., Su, L., Dornhaus, A., Lynch, N. (2017). Ant-Inspired Dynamic Task Allocation via Gossiping. In: Spirakis, P., Tsigas, P. (eds) Stabilization, Safety, and Security of Distributed Systems. SSS 2017. Lecture Notes in Computer Science(), vol 10616. Springer, Cham. https://doi.org/10.1007/978-3-319-69084-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-69084-1_11

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