Abstract
Automation is crucial in increasingly many workplaces. Though automation is often associated with job replacement, humans and machines have divergent proficiencies. Thus, human-machine teaming is generally favored over replacement. Within applied surveillance environments, automation is leveraged for cognitively intensive tasks. To maintain optimal performance within a dyadic human-machine team, we developed an Autonomous Manager (AM) that dynamically redistributes tasks between human and machine. Participants performed four simultaneous image identification tasks while paired with a simulated autonomous partner. Our AM was responsible for monitoring team performance and redistributing tasks when performance fell sub-threshold. We manipulated the refresh rate of the images, affording us the opportunity to measure improvement under multiple conditions.
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Frame, M.E., Boydstun, A.S., Maresca, A.M., Lopez, J.S. (2019). Development of an Autonomous Manager for Dyadic Human-Machine Teams in an Applied Multitasking Surveillance Environment. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_107
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DOI: https://doi.org/10.1007/978-3-030-11051-2_107
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-030-11051-2
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