The Importance of Information Flow Regulation in Preferentially Foraging Robot Swarms

Lenka Pitonakova, Richard Crowder, Seth Bullock

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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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.
Original languageEnglish
Title of host publicationSwarm Intelligence
Subtitle of host publication11th International Conference, ANTS 2018, Rome, Italy, October 29–31, 2018, Proceedings
PublisherSpringer, Cham
Number of pages13
ISBN (Electronic)9783030005337
ISBN (Print)9783030005320
Publication statusPublished - 3 Oct 2018
EventEleventh International Conference on Swarm Intelligence (ANTS 2018) - Rome, Italy
Duration: 29 Oct 201831 Oct 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceEleventh International Conference on Swarm Intelligence (ANTS 2018)
Internet address


  • swarm intelligence
  • robotics
  • Foraging


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