The importance of information flow regulation in preferentially foraging robot swarms
The importance of information flow regulation in preferentially foraging robot swarms
Instead of committing to the first source of reward that it discovers, an agent engaged in "preferential foraging" continues to choose between different reward sources in order to maximise its foraging efficiency. In this paper, the effect of preferential source selection on the performance of robot swarms with different recruitment strategies is studied. The swarms are tasked with foraging from multiple sources in dynamic environments where worksite locations change periodically and thus need to be re-discovered. Analysis indicates that preferential foraging leads to a more even exploitation of resources and a more efficient exploration of the environment provided that information
flow among robots, that results from recruitment, is regulated. On the other hand, preferential selection acts as a strong positive feedback mechanism for favouring the most popular reward source when robots exchange information rapidly in a small designated area, preventing the swarm from foraging efficiently and from responding to changes.
277-289
Pitonakova, Lenka
ef806152-a9c0-4075-806d-c75f0d3f7bbb
Crowder, Richard
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Bullock, Seth
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Pitonakova, Lenka
ef806152-a9c0-4075-806d-c75f0d3f7bbb
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023
Bullock, Seth
2ad576e4-56b8-4f31-84e0-51bd0b7a1cd3
Pitonakova, Lenka, Crowder, Richard and Bullock, Seth
(2018)
The importance of information flow regulation in preferentially foraging robot swarms.
Dorigo, Marco, Birattari, Mauro, Blum, Christian, Christensen, Anders L., Reina, Andreagiovanni and Trianni, Vito
(eds.)
In Swarm Intelligence: 11th International Conference, ANTS 2018, Rome, Italy, October 29–31, 2018, Proceedings.
vol. 11172,
Springer.
.
(doi:10.1007/978-3-030-00533-7_22).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Instead of committing to the first source of reward that it discovers, an agent engaged in "preferential foraging" continues to choose between different reward sources in order to maximise its foraging efficiency. In this paper, the effect of preferential source selection on the performance of robot swarms with different recruitment strategies is studied. The swarms are tasked with foraging from multiple sources in dynamic environments where worksite locations change periodically and thus need to be re-discovered. Analysis indicates that preferential foraging leads to a more even exploitation of resources and a more efficient exploration of the environment provided that information
flow among robots, that results from recruitment, is regulated. On the other hand, preferential selection acts as a strong positive feedback mechanism for favouring the most popular reward source when robots exchange information rapidly in a small designated area, preventing the swarm from foraging efficiently and from responding to changes.
Text
Pitonakova_preferentialForaging
- Accepted Manuscript
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Accepted/In Press date: 2 July 2018
e-pub ahead of print date: 3 October 2018
Identifiers
Local EPrints ID: 423165
URI: http://eprints.soton.ac.uk/id/eprint/423165
PURE UUID: 6d432673-5368-49b3-99c3-f1ba186c7524
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Date deposited: 19 Sep 2018 16:30
Last modified: 16 Mar 2024 07:05
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Contributors
Author:
Lenka Pitonakova
Author:
Richard Crowder
Editor:
Marco Dorigo
Editor:
Mauro Birattari
Editor:
Christian Blum
Editor:
Anders L. Christensen
Editor:
Andreagiovanni Reina
Editor:
Vito Trianni
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