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.
Iryna Dzieciuch, John Reeder, Robert Gutzwiller, Eric Gustafson, Braulio Coronado, Luis Martinez, Bryan Croft, and 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 (Presented at SPIE Defense + Security: April 12, 2017; Published: 18 May 2017); https://doi.org/10.1117/12.2262849.
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