2009 Special Issue: Coordinated machine learning and decision support for situation awareness

Authors:
N. G. Brannon;J. E. Seiffertt;T. J. Draelos;D. C. Wunsch, II
Affiliations:
Reliability Assessment and Human Systems Integration Department, Sandia National Laboratories, Albuquerque, NM 87185, USA11Sandia National Laboratories is a multi-program laboratory operated by Sa ...;Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA;Cryptography and Information Systems Surety Department, Sandia National Laboratories, Albuquerque, NM 87185, USA;Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
Venue:
Neural Networks
Year:
2009

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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.