Title

Coordinated Machine Learning and Decision Support for Situation Awareness

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.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

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

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 Elsevier, All rights reserved.


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