Skip to main content

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: HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. New York: Association for Computing Machinery, 172-176.

Full text available as:

3610978.3640725.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.1145/3610978.3640725


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:Book Section
Uncontrolled Keywords:Human-Robot Interaction (HRI); Human-Swarm Interaction (HSI); Predictive Formal Modelling (PFM); Task Performance
Group:Faculty of Science & Technology
ID Code:39774
Deposited By: Symplectic RT2
Deposited On:01 May 2024 07:30
Last Modified:01 May 2024 07:30


Downloads per month over past year

More statistics for this item...
Repository Staff Only -