18 May 2017 Amplifying human ability through autonomics and machine learning in IMPACT
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Abstract
Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.
Conference Presentation
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Iryna Dzieciuch, Iryna Dzieciuch, John Reeder, John Reeder, Robert Gutzwiller, Robert Gutzwiller, Eric Gustafson, Eric Gustafson, Braulio Coronado, Braulio Coronado, Luis Martinez, Luis Martinez, Bryan Croft, Bryan Croft, Douglas S. Lange, Douglas S. Lange, } "Amplifying human ability through autonomics and machine learning in IMPACT", Proc. SPIE 10194, Micro- and Nanotechnology Sensors, Systems, and Applications IX, 101941Y (18 May 2017); doi: 10.1117/12.2262849; https://doi.org/10.1117/12.2262849
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