Amplifying human ability through autonomics and machine learning in IMPACT

Iryna Dzieciuch, John Reeder, Robert Gutzwiller, Eric Gustafson, Braulio Coronado, Luis Martinez, Bryan Croft, Douglas S. Lange

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMicro- and Nanotechnology Sensors, Systems, and Applications IX
EditorsAchyut K. Dutta, M. Saif Islam, Thomas George
PublisherSPIE
ISBN (Electronic)9781510608894
DOIs
StatePublished - Jan 1 2017
Externally publishedYes
EventMicro- and Nanotechnology Sensors, Systems, and Applications IX 2017 - Anaheim, United States
Duration: Apr 9 2017Apr 13 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10194
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherMicro- and Nanotechnology Sensors, Systems, and Applications IX 2017
Country/TerritoryUnited States
CityAnaheim
Period4/9/174/13/17

Keywords

  • Autonomics
  • Machine Learning
  • Plan Monitoring
  • Task Management

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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