Demonstrating the Differential Impact of Flock Heterogeneity on Multi-agent Herding

Chris Bennett*, Seth Bullock, Jonathan Lawry

*Corresponding author for this work

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

Abstract

This paper explores the differential impact of multi-agent system heterogeneity in the context of an idealised herding task. In simulation, a team of simple herders must move a flock towards a target location in a continuous 2d space. Flock heterogeneity is controlled by dividing the flock into a number of non-overlapping social groups that influence sheep movement. Results demonstrate that increasing system heterogeneity (i.e., the number of different social groups) reduces herding performance when social groups are self-attracting, but conversely, the same increase in system heterogeneity can increase herding performance when groups are other-attracting. Implications for designing heterogeneous multi-agent systems are considered.
Original languageEnglish
Title of host publicationAnnual Conference Towards Autonomous Robotic Systems
Subtitle of host publicationTAROS 2021: Towards Autonomous Robotic Systems
PublisherSpringer
Pages147-157
Number of pages11
ISBN (Electronic)978-3-030-89177-0
ISBN (Print)978-3-030-89176-3
DOIs
Publication statusPublished - 31 Oct 2021
EventTowards Autonomous Robotic Systems Conference - Lincoln, United Kingdom
Duration: 8 Sep 202110 Sep 2021
https://lcas.lincoln.ac.uk/wp/taros-2021/

Conference

ConferenceTowards Autonomous Robotic Systems Conference
Abbreviated titleTAROS 2021
Country/TerritoryUnited Kingdom
CityLincoln
Period8/09/2110/09/21
Internet address

Bibliographical note

Funding Information:
This work was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled ‘Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)’.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Heterogeneous Agents
  • Multi-Agent System
  • Herding

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