The effect of predictive formal modelling at runtime on performance in human-swarm interaction
The effect of predictive formal modelling at runtime on performance in human-swarm interaction
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.
Human-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Task Performance
172–176
Association for Computing Machinery
Abioye, Ayodeji O.
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Hunt, William
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Gu, Yue
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Schneiders, Eike
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Naiseh, Mohammad
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Fischer, Joel E.
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Ramchurn, Sarvapali D.
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Soorati, Mohammad D.
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Archibald, Blair
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Sevegnani, Michele
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11 March 2024
Abioye, Ayodeji O.
3ec89a0b-8e78-4ef6-a1d7-342d8f6da021
Hunt, William
eec4ba79-8870-4657-a2ea-25511ae9dbaa
Gu, Yue
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Schneiders, Eike
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Naiseh, Mohammad
2c521cc6-0405-45f8-8d55-7d14eb4e4126
Fischer, Joel E.
34bd3ac9-a2f2-41ba-9207-0667513bfa84
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Soorati, Mohammad D.
35fe6bbb-ce52-4c21-a46e-9bb0e31d246c
Archibald, Blair
68d001d1-5df4-414d-96ef-b2f45080b4e8
Sevegnani, Michele
f29f5049-e7a3-4086-b624-104ac94195b4
Abioye, Ayodeji O., Hunt, William, Gu, Yue, Schneiders, Eike, Naiseh, Mohammad, Fischer, Joel E., Ramchurn, Sarvapali D., Soorati, Mohammad D., Archibald, Blair and Sevegnani, Michele
(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.
Association for Computing Machinery.
.
(doi:10.1145/3610978.3640725).
Record type:
Conference or Workshop Item
(Paper)
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.
Text
HRI24lbr_1074
- Accepted Manuscript
Text
3610978.3640725
- Version of Record
More information
Published date: 11 March 2024
Venue - Dates:
The 2024 ACM/IEEE International Conference on Human-Robot Interaction, University of Colorado, Boulder, Boulder, United States, 2024-03-11 - 2024-03-15
Keywords:
Human-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Task Performance
Identifiers
Local EPrints ID: 488273
URI: http://eprints.soton.ac.uk/id/eprint/488273
PURE UUID: d7648dbf-5ec9-4b63-adfd-478634b6b1d2
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Date deposited: 19 Mar 2024 17:51
Last modified: 27 Apr 2024 02:06
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Contributors
Author:
Ayodeji O. Abioye
Author:
William Hunt
Author:
Yue Gu
Author:
Eike Schneiders
Author:
Mohammad Naiseh
Author:
Joel E. Fischer
Author:
Sarvapali D. Ramchurn
Author:
Mohammad D. Soorati
Author:
Blair Archibald
Author:
Michele Sevegnani
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