Coordinated Machine Learning and Decision Support for Situation Awareness
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.
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 http://dx.doi.org/10.1016/j.neunet.2009.03.013
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Resonance Theory; Neural Networks; Reinforcement Learning; Situation Awareness
Article - Journal
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