font
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Archibald, Blair; Sevegnani, Michele; Ramchurn, Sarvapali D.; Fischer, Joel E.; Soorati, Mohammad D.
A user study evaluation of predictive formal modelling at runtime in human-swarm interaction Journal Article
In: ACM Transactions on Human-Robot Interaction, vol. 14, no. 4, 2025.
Abstract | Links | BibTeX | Tags: Additional Key Words and PhrasesHuman-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Usability, Workload
@article{soton501672,
title = {A user study evaluation of predictive formal modelling at runtime in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Blair Archibald and Michele Sevegnani and Sarvapali D. Ramchurn and Joel E. Fischer and Mohammad D. Soorati},
url = {https://eprints.soton.ac.uk/501672/},
year = {2025},
date = {2025-06-01},
journal = {ACM Transactions on Human-Robot Interaction},
volume = {14},
number = {4},
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. We conducted a user study evaluation of predictive formal modelling (PFM) at runtime in a human-swarm mission to determine the benefit of predictive formal modelling on performance and human-swarm interaction. 180 participants were recruited to perform the role of aerial swarm operators delivering parcels to target locations in a simulation environment. The PFM model was integrated into the simulation software to inform the operator of the estimated mission completion time given the current number of drones deployed. The operator could increase the number of parcels delivered in any time step by adding drones, which also increased costs, thus requiring the use of the minimum number of drones necessary to complete the task in the given time. We collected user feedback using standard survey questionnaires and measured performance using data obtained from the Human And Robot Interactive Swarm (HARIS) simulator. Our results show that PFM increased the performance of the human swarm team without significantly increasing the operators? workload or affecting the system's usability.},
keywords = {Additional Key Words and PhrasesHuman-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Usability, Workload},
pubstate = {published},
tppubtype = {article}
}
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Fischer, Joel E.; Ramchurn, Sarvapali D.; Soorati, Mohammad D.; Archibald, Blair; Sevegnani, Michele
The effect of predictive formal modelling at runtime on performance in human-swarm interaction Proceedings Article
In: HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, pp. 172?176, Association for Computing Machinery, 2024, (Publisher Copyright: © 2024 Copyright held by the owner/author(s)).
Abstract | Links | BibTeX | Tags: Human-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Task Performance
@inproceedings{soton488273,
title = {The effect of predictive formal modelling at runtime on performance in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Joel E. Fischer and Sarvapali D. Ramchurn and Mohammad D. Soorati and Blair Archibald and Michele Sevegnani},
url = {https://eprints.soton.ac.uk/488273/},
year = {2024},
date = {2024-03-01},
booktitle = {HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction},
pages = {172?176},
publisher = {Association for Computing Machinery},
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.},
note = {Publisher Copyright:
© 2024 Copyright held by the owner/author(s)},
keywords = {Human-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Task Performance},
pubstate = {published},
tppubtype = {inproceedings}
}
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Archibald, Blair; Sevegnani, Michele; Ramchurn, Sarvapali D.; Fischer, Joel E.; Soorati, Mohammad D.
A user study evaluation of predictive formal modelling at runtime in human-swarm interaction Journal Article
In: ACM Transactions on Human-Robot Interaction, vol. 14, no. 4, 2025.
@article{soton501672,
title = {A user study evaluation of predictive formal modelling at runtime in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Blair Archibald and Michele Sevegnani and Sarvapali D. Ramchurn and Joel E. Fischer and Mohammad D. Soorati},
url = {https://eprints.soton.ac.uk/501672/},
year = {2025},
date = {2025-06-01},
journal = {ACM Transactions on Human-Robot Interaction},
volume = {14},
number = {4},
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. We conducted a user study evaluation of predictive formal modelling (PFM) at runtime in a human-swarm mission to determine the benefit of predictive formal modelling on performance and human-swarm interaction. 180 participants were recruited to perform the role of aerial swarm operators delivering parcels to target locations in a simulation environment. The PFM model was integrated into the simulation software to inform the operator of the estimated mission completion time given the current number of drones deployed. The operator could increase the number of parcels delivered in any time step by adding drones, which also increased costs, thus requiring the use of the minimum number of drones necessary to complete the task in the given time. We collected user feedback using standard survey questionnaires and measured performance using data obtained from the Human And Robot Interactive Swarm (HARIS) simulator. Our results show that PFM increased the performance of the human swarm team without significantly increasing the operators? workload or affecting the system's usability.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Fischer, Joel E.; Ramchurn, Sarvapali D.; Soorati, Mohammad D.; Archibald, Blair; Sevegnani, Michele
The effect of predictive formal modelling at runtime on performance in human-swarm interaction Proceedings Article
In: HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, pp. 172?176, Association for Computing Machinery, 2024, (Publisher Copyright: © 2024 Copyright held by the owner/author(s)).
@inproceedings{soton488273,
title = {The effect of predictive formal modelling at runtime on performance in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Joel E. Fischer and Sarvapali D. Ramchurn and Mohammad D. Soorati and Blair Archibald and Michele Sevegnani},
url = {https://eprints.soton.ac.uk/488273/},
year = {2024},
date = {2024-03-01},
booktitle = {HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction},
pages = {172?176},
publisher = {Association for Computing Machinery},
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.},
note = {Publisher Copyright:
© 2024 Copyright held by the owner/author(s)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Archibald, Blair; Sevegnani, Michele; Ramchurn, Sarvapali D.; Fischer, Joel E.; Soorati, Mohammad D.
A user study evaluation of predictive formal modelling at runtime in human-swarm interaction Journal Article
In: ACM Transactions on Human-Robot Interaction, vol. 14, no. 4, 2025.
Abstract | Links | BibTeX | Tags: Additional Key Words and PhrasesHuman-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Usability, Workload
@article{soton501672,
title = {A user study evaluation of predictive formal modelling at runtime in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Blair Archibald and Michele Sevegnani and Sarvapali D. Ramchurn and Joel E. Fischer and Mohammad D. Soorati},
url = {https://eprints.soton.ac.uk/501672/},
year = {2025},
date = {2025-06-01},
journal = {ACM Transactions on Human-Robot Interaction},
volume = {14},
number = {4},
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. We conducted a user study evaluation of predictive formal modelling (PFM) at runtime in a human-swarm mission to determine the benefit of predictive formal modelling on performance and human-swarm interaction. 180 participants were recruited to perform the role of aerial swarm operators delivering parcels to target locations in a simulation environment. The PFM model was integrated into the simulation software to inform the operator of the estimated mission completion time given the current number of drones deployed. The operator could increase the number of parcels delivered in any time step by adding drones, which also increased costs, thus requiring the use of the minimum number of drones necessary to complete the task in the given time. We collected user feedback using standard survey questionnaires and measured performance using data obtained from the Human And Robot Interactive Swarm (HARIS) simulator. Our results show that PFM increased the performance of the human swarm team without significantly increasing the operators? workload or affecting the system's usability.},
keywords = {Additional Key Words and PhrasesHuman-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Usability, Workload},
pubstate = {published},
tppubtype = {article}
}
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Fischer, Joel E.; Ramchurn, Sarvapali D.; Soorati, Mohammad D.; Archibald, Blair; Sevegnani, Michele
The effect of predictive formal modelling at runtime on performance in human-swarm interaction Proceedings Article
In: HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, pp. 172?176, Association for Computing Machinery, 2024, (Publisher Copyright: © 2024 Copyright held by the owner/author(s)).
Abstract | Links | BibTeX | Tags: Human-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Task Performance
@inproceedings{soton488273,
title = {The effect of predictive formal modelling at runtime on performance in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Joel E. Fischer and Sarvapali D. Ramchurn and Mohammad D. Soorati and Blair Archibald and Michele Sevegnani},
url = {https://eprints.soton.ac.uk/488273/},
year = {2024},
date = {2024-03-01},
booktitle = {HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction},
pages = {172?176},
publisher = {Association for Computing Machinery},
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.},
note = {Publisher Copyright:
© 2024 Copyright held by the owner/author(s)},
keywords = {Human-Robot Interaction (HRI), Human-Swarm Interaction (HSI), Predictive Formal Modelling (PFM), Task Performance},
pubstate = {published},
tppubtype = {inproceedings}
}
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Archibald, Blair; Sevegnani, Michele; Ramchurn, Sarvapali D.; Fischer, Joel E.; Soorati, Mohammad D.
A user study evaluation of predictive formal modelling at runtime in human-swarm interaction Journal Article
In: ACM Transactions on Human-Robot Interaction, vol. 14, no. 4, 2025.
@article{soton501672,
title = {A user study evaluation of predictive formal modelling at runtime in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Blair Archibald and Michele Sevegnani and Sarvapali D. Ramchurn and Joel E. Fischer and Mohammad D. Soorati},
url = {https://eprints.soton.ac.uk/501672/},
year = {2025},
date = {2025-06-01},
journal = {ACM Transactions on Human-Robot Interaction},
volume = {14},
number = {4},
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. We conducted a user study evaluation of predictive formal modelling (PFM) at runtime in a human-swarm mission to determine the benefit of predictive formal modelling on performance and human-swarm interaction. 180 participants were recruited to perform the role of aerial swarm operators delivering parcels to target locations in a simulation environment. The PFM model was integrated into the simulation software to inform the operator of the estimated mission completion time given the current number of drones deployed. The operator could increase the number of parcels delivered in any time step by adding drones, which also increased costs, thus requiring the use of the minimum number of drones necessary to complete the task in the given time. We collected user feedback using standard survey questionnaires and measured performance using data obtained from the Human And Robot Interactive Swarm (HARIS) simulator. Our results show that PFM increased the performance of the human swarm team without significantly increasing the operators? workload or affecting the system's usability.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Fischer, Joel E.; Ramchurn, Sarvapali D.; Soorati, Mohammad D.; Archibald, Blair; Sevegnani, Michele
The effect of predictive formal modelling at runtime on performance in human-swarm interaction Proceedings Article
In: HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, pp. 172?176, Association for Computing Machinery, 2024, (Publisher Copyright: © 2024 Copyright held by the owner/author(s)).
@inproceedings{soton488273,
title = {The effect of predictive formal modelling at runtime on performance in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Joel E. Fischer and Sarvapali D. Ramchurn and Mohammad D. Soorati and Blair Archibald and Michele Sevegnani},
url = {https://eprints.soton.ac.uk/488273/},
year = {2024},
date = {2024-03-01},
booktitle = {HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction},
pages = {172?176},
publisher = {Association for Computing Machinery},
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.},
note = {Publisher Copyright:
© 2024 Copyright held by the owner/author(s)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Multi-agent signal-less intersection management with dynamic platoon formation
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response
The effect of data visualisation quality and task density on human-swarm interaction
Demonstrating performance benefits of human-swarm teaming
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Archibald, Blair; Sevegnani, Michele; Ramchurn, Sarvapali D.; Fischer, Joel E.; Soorati, Mohammad D.
A user study evaluation of predictive formal modelling at runtime in human-swarm interaction Journal Article
In: ACM Transactions on Human-Robot Interaction, vol. 14, no. 4, 2025.
@article{soton501672,
title = {A user study evaluation of predictive formal modelling at runtime in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Blair Archibald and Michele Sevegnani and Sarvapali D. Ramchurn and Joel E. Fischer and Mohammad D. Soorati},
url = {https://eprints.soton.ac.uk/501672/},
year = {2025},
date = {2025-06-01},
journal = {ACM Transactions on Human-Robot Interaction},
volume = {14},
number = {4},
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. We conducted a user study evaluation of predictive formal modelling (PFM) at runtime in a human-swarm mission to determine the benefit of predictive formal modelling on performance and human-swarm interaction. 180 participants were recruited to perform the role of aerial swarm operators delivering parcels to target locations in a simulation environment. The PFM model was integrated into the simulation software to inform the operator of the estimated mission completion time given the current number of drones deployed. The operator could increase the number of parcels delivered in any time step by adding drones, which also increased costs, thus requiring the use of the minimum number of drones necessary to complete the task in the given time. We collected user feedback using standard survey questionnaires and measured performance using data obtained from the Human And Robot Interactive Swarm (HARIS) simulator. Our results show that PFM increased the performance of the human swarm team without significantly increasing the operators? workload or affecting the system's usability.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abioye, Ayodeji O.; Hunt, William; Gu, Yue; Schneiders, Eike; Naiseh, Mohammad; Fischer, Joel E.; Ramchurn, Sarvapali D.; Soorati, Mohammad D.; Archibald, Blair; Sevegnani, Michele
The effect of predictive formal modelling at runtime on performance in human-swarm interaction Proceedings Article
In: HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, pp. 172?176, Association for Computing Machinery, 2024, (Publisher Copyright: © 2024 Copyright held by the owner/author(s)).
@inproceedings{soton488273,
title = {The effect of predictive formal modelling at runtime on performance in human-swarm interaction},
author = {Ayodeji O. Abioye and William Hunt and Yue Gu and Eike Schneiders and Mohammad Naiseh and Joel E. Fischer and Sarvapali D. Ramchurn and Mohammad D. Soorati and Blair Archibald and Michele Sevegnani},
url = {https://eprints.soton.ac.uk/488273/},
year = {2024},
date = {2024-03-01},
booktitle = {HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction},
pages = {172?176},
publisher = {Association for Computing Machinery},
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.},
note = {Publisher Copyright:
© 2024 Copyright held by the owner/author(s)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}