@inproceedings{f33370ef3cba47eeb2c36d52ca3158a3,
title = "Amplifying human ability through autonomics and machine learning in IMPACT",
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.",
keywords = "Autonomics, Machine Learning, Plan Monitoring, Task Management",
author = "Iryna Dzieciuch and John Reeder and Robert Gutzwiller and Eric Gustafson and Braulio Coronado and Luis Martinez and Bryan Croft and Lange, {Douglas S.}",
year = "2017",
month = jan,
day = "1",
doi = "10.1117/12.2262849",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Dutta, {Achyut K.} and Islam, {M. Saif} and Thomas George",
booktitle = "Micro- and Nanotechnology Sensors, Systems, and Applications IX",
note = "Micro- and Nanotechnology Sensors, Systems, and Applications IX 2017 ; Conference date: 09-04-2017 Through 13-04-2017",
}