Detecting and classifying degradation in robotic swarms: An experimental study

Seth Bullock*, Jan M Noyes, Victoria Steane, Chris Bennett, Wenwen Gao, Sophie G Hart, Elliott Hogg, Debora Zanatto

*Corresponding author for this work

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

Abstract

This paper describes the results of an experiment in which human participants were required to detect degraded robot swarm behaviour and classify it as arising from either faulty or malicious robot activity in an idealised simulation of a multi-agent search and rescue task. The accuracy of participant judgements was influenced by the nature of the degradation, and between-participant differences in the extent to which they interacted with the swarm did not significantly influence their accuracy. It was found that detecting and classifying swarm degradation are challenging tasks that are likely to be strongly sensitive to task setting and will tend to require careful swarm system design and specific operator training.
Original languageEnglish
Title of host publicationALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference
EditorsHero Iizuka, Keisuke Suzuki, Ryoko Uno, Luisa Damiano, N Spychalav, Miguel Aguilera, Eduardo Izquierdo, Reiji Suzuki, Manuel Baltieri
PublisherMassachusetts Institute of Technology (MIT) Press
Number of pages3
DOIs
Publication statusPublished - 24 Jul 2023
EventThe 2023 Conference on Artificial Life - Sapporo, Japan
Duration: 24 Jul 202328 Jul 2023

Publication series

NameArtificial Life Conference Proceedings
PublisherMIT Press
ISSN (Electronic)2693-1508
NameALIFE : proceedings of the artificial life conference
NameProceedings of the artificial life conference

Conference

ConferenceThe 2023 Conference on Artificial Life
Country/TerritoryJapan
CitySapporo
Period24/07/2328/07/23

Keywords

  • swarm robotics
  • human robot collaboration
  • psychology
  • experiment
  • fault detection

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