Coordinated Machine Learning and Decision Support for Situation Awareness

Author

N. G. Brannon
John E. Seiffertt IV, Missouri University of Science and TechnologyFollow
T. J. Draelos
Donald C. Wunsch, Missouri University of Science and TechnologyFollow

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.

Recommended Citation

N. G. Brannon et al., "Coordinated Machine Learning and Decision Support for Situation Awareness," Neural Networks, Elsevier, Apr 2009.

The definitive version is available at https://doi.org/10.1016/j.neunet.2009.03.013

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Adaptive Resonance Theory; Neural Networks; Reinforcement Learning; Situation Awareness

International Standard Serial Number (ISSN)

0893-6080

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 Elsevier, All rights reserved.

Publication Date

01 Apr 2009