The Effect of Predictive Formal Modelling at Runtime on Performance in Human-Swarm Interaction

Abioye, A. O., Hunt, W., Gu, Y., Schneiders, E., Naiseh, M., Fischer, J. E., Ramchurn, S. D., Soorati, M. D., Archibald, B. and Sevegnani, M. (2024) The Effect of Predictive Formal Modelling at Runtime on Performance in Human-Swarm Interaction. In: 19th Annual ACM/IEEE International Conference on Human Robot Interaction (HRI 2024), Boulder, Colorado, USA, 11-15 March 2024, pp. 172-176. ISBN 9798400703232 (doi: 10.1145/3610978.3640725)

Abstract

Formal Modelling is often used as part of the design and testing process of software development to ensure that components operate within suitable bounds even in unexpected circumstances. In this paper, we use predictive formal modelling (PFM) at runtime in a human-swarm mission and show that this integration can be used to improve the performance of human-swarm teams. We recruited 60 participants to operate a simulated aerial swarm to deliver parcels to target locations. In the PFM condition, operators were informed of the estimated completion times given the number of drones deployed, whereas, in the No-PFM condition, operators did not have this information. The operators could control the mission by adding or removing drones from the mission and thereby, increasing or decreasing the overall mission cost. The evaluation of human-swarm performance relied on four metrics: the task completion time, the number of agents, the number of completed tasks, and the cost per task. Our results show that PFM modelling at runtime improves mission performance without significantly affecting the operator's workload or the system's usability.

• Item Type: Conference Proceedings
• Additional Information: We wish to acknowledge support from the EPSRC project on Smart Solutions Towards Cellular-Connected UAV System (EP/W004364/1), the UKRI Trustworthy Autonomous Systems Hub (EP/V00784X/1), and an Amazon Research Award on Automated Reasoning.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gu, Dr Yue and Sevegnani, Dr Michele and Archibald, Dr Blair
• Authors: Abioye, A. O., Hunt, W., Gu, Y., Schneiders, E., Naiseh, M., Fischer, J. E., Ramchurn, S. D., Soorati, M. D., Archibald, B., and Sevegnani, M.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9798400703232
• Copyright Holders: Copyright: © 2024 Copyright held by the owner/author(s).
• First Published: First published in Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction: 172-176
• Publisher Policy: Reproduced under a Creative Commons licence

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