The influence of memory in a threshold model for distributed task assignment

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task selection in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without a priori knowledge of the available mail at the cities or inter-agent communication. In order to process a different mail type than the previous one, agents must undergo a change-over during which it remains inactive. We propose a threshold based algorithm in order to maximise the overall efficiency (the average amount of mail collected). We show that memory, i.e. the possibility for agents to develop preferences for certain cities, not only leads to emergent cooperation between agents, but also to a significant increase in efficiency (above the theoretical upper limit for any memoryless algorithm), and we systematically investigate the influence of the various model parameters. Finally, we demonstrate the flexibility of the algorithm to changes in circumstances, and its excellent scalability.
Original languageEnglish
Title of host publicationSASO '08: 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Place of PublicationWashington (US)
PublisherIEEE
Pages117-126
Number of pages10
ISBN (Print)978-0-7695-3404-6
DOIs
Publication statusPublished - 20 Oct 2008

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Communication

Bibliographical note

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

Keywords

  • distributed decision-making
  • memory
  • response thresholds
  • self organisation

Cite this

Goldingay, H., & van Mourik, J. (2008). The influence of memory in a threshold model for distributed task assignment. In SASO '08: 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems (pp. 117-126). Washington (US): IEEE. https://doi.org/10.1109/SASO.2008.37
Goldingay, Harry ; van Mourik, Jort. / The influence of memory in a threshold model for distributed task assignment. SASO '08: 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems. Washington (US) : IEEE, 2008. pp. 117-126
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Goldingay, H & van Mourik, J 2008, The influence of memory in a threshold model for distributed task assignment. in SASO '08: 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems. IEEE, Washington (US), pp. 117-126. https://doi.org/10.1109/SASO.2008.37

The influence of memory in a threshold model for distributed task assignment. / Goldingay, Harry; van Mourik, Jort.

SASO '08: 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems. Washington (US) : IEEE, 2008. p. 117-126.

Research output: Chapter in Book/Report/Conference proceedingChapter

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PY - 2008/10/20

Y1 - 2008/10/20

N2 - A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task selection in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without a priori knowledge of the available mail at the cities or inter-agent communication. In order to process a different mail type than the previous one, agents must undergo a change-over during which it remains inactive. We propose a threshold based algorithm in order to maximise the overall efficiency (the average amount of mail collected). We show that memory, i.e. the possibility for agents to develop preferences for certain cities, not only leads to emergent cooperation between agents, but also to a significant increase in efficiency (above the theoretical upper limit for any memoryless algorithm), and we systematically investigate the influence of the various model parameters. Finally, we demonstrate the flexibility of the algorithm to changes in circumstances, and its excellent scalability.

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Goldingay H, van Mourik J. The influence of memory in a threshold model for distributed task assignment. In SASO '08: 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems. Washington (US): IEEE. 2008. p. 117-126 https://doi.org/10.1109/SASO.2008.37