The Effect of Predictive Formal Modelling at Runtime on Performance in Human-Swarm Interaction
IEEE/ACM International Conference on Human-Robot Interaction(2024)
摘要
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 key metrics: the time taken to complete
tasks, the number of agents involved, the total number of tasks accomplished,
and the overall cost associated with the human-swarm task. Our results show
that PFM modelling at runtime improves mission performance without
significantly affecting the operator's workload or the system's usability.
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