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Proceedings Paper

Amplifying human ability through autonomics and machine learning in IMPACT
Author(s): Iryna Dzieciuch; John Reeder; Robert Gutzwiller; Eric Gustafson; Braulio Coronado; Luis Martinez; Bryan Croft; Douglas S. Lange
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Paper 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.

Paper Details

Date Published: 18 May 2017
PDF: 11 pages
Proc. SPIE 10194, Micro- and Nanotechnology Sensors, Systems, and Applications IX, 101941Y (18 May 2017); doi: 10.1117/12.2262849
Show Author Affiliations
Iryna Dzieciuch, SPAWAR Systems Ctr. Pacific (United States)
John Reeder, SPAWAR Systems Ctr. Pacific (United States)
Robert Gutzwiller, SPAWAR Systems Ctr. Pacific (United States)
Eric Gustafson, SPAWAR Systems Ctr. Pacific (United States)
Braulio Coronado, SPAWAR Systems Ctr. Pacific (United States)
Luis Martinez, SPAWAR Systems Ctr. Pacific (United States)
Bryan Croft, SPAWAR Systems Ctr. Pacific (United States)
Douglas S. Lange, SPAWAR Systems Ctr. Pacific (United States)


Published in SPIE Proceedings Vol. 10194:
Micro- and Nanotechnology Sensors, Systems, and Applications IX
Thomas George; Achyut K. Dutta; M. Saif Islam, Editor(s)

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