A4 Refereed article in a conference publication
Reimagining Trustworthy Robot Fleets with Animal Analogies
Authors: Hyökki, Suvi; Phillips, Elizabeth K.; Melles, Lydia; Laakasuo, Michael
Editors: Gray, Colin M.; Ciliotta Chehade, Estefania; Hekkert, Paul; Forlano, Laura; Ciuccarelli, Paolo; Lloyd, Peter
Conference name: Design Research Society Conference
Publisher: Design Research Society
Publication year: 2024
Journal: Proceedings of DRS : Design Research Society International Conference
Book title : DRS2024 : Boston
Article number: 196
Volume: 2024
ISBN: 978-1-912294-62-6
eISSN: 2398-3132
DOI: https://doi.org/10.21606/drs.2024.718
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://doi.org/10.21606/drs.2024.718
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/508973159
Self-archived copy's licence: CC BY NC
Self-archived copy's version: Publisher`s PDF
In the future, multi-agent robot fleets will be important for domains like agriculture, space exploration, and air combat. Trust of human-machine teams is needed to make the teams resilient to the faults of both human and robot teammates. Trust in multi-agent systems is often fragile: if any agent in the system is less reliable than the others, people will stop interacting with all of them. Studying relationships in human-animal systems can provide useful insights into designing humanrobot systems. We present a method for gathering insight into how humans, working with animal systems think about the relationships between the individuals and the whole, and suggest how animal system models can be used as analogies and practical design features for the design of robot systems in order to increase trust. Using a more-than-human approach in design research phase of human-robot interaction, supports more secure collaboration between humans and robot systems.
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Funding information in the publication:
Part of the work in this study was funded by Jane and Aatos Erkko Foundation. This work was also supported in part by George Mason University’s Office of Research, Innovation, and Economic Impact (ORIEI) award #215134.
