Article
Coordinated machine learning and decision support for situation awareness
Neural Networks
(2009)
Abstract
Domains such as force protection require an effective decision maker to maintain a high level of situation awareness. A system that combines humans with neural networks is a desirable approach. Furthermore, it is advantageous for the calculation engine to operate in three learning modes: supervised for initial training and known updating, reinforcement for online operational improvement, and unsupervised in the absence of all external signaling. An Adaptive Resonance Theory based architecture capable of seamlessly switching among the three types of learning is discussed that can be used to help optimize the decision making of a human operator in such a scenario. This is followed by a situation assessment module.
Disciplines
Publication Date
April, 2009
DOI
https://doi.org/10.1016/j.neunet.2009.03.013
Citation Information
John Seiffertt, N G Brannon, Donald C. Wunsch and T J Draelos. "Coordinated machine learning and decision support for situation awareness" Neural Networks Vol. 22 Iss. 3 (2009) p. 316 - 325 ISSN: 0893-6080 Available at: http://works.bepress.com/john-seiffertt/5/