Coordinated machine learning and decision support for situation awareness

被引：32

作者：

Brannon, N. G. ^{[2
]}

Seiffertt, J. E. ^{[1
]}

Draelos, T. J. ^{[3
]}

Il, D. C. Wunsch ^{[1
]}

机构：

[1] Missouri Univ Sci & Technol, Dept Elect & Comp Engn, Appl Computat Intelligence Lab, Rolla, MO 65409 USA

[2] Sandia Natl Labs, Reliabil Assessment & Human Syst Integrat Dept, Albuquerque, NM 87185 USA

[3] Sandia Natl Labs, Cryptog & Informat Syst Surety Dept, Albuquerque, NM 87185 USA

来源：

NEURAL NETWORKS | 2009年 / 22卷 / 03期

关键词：

Neural networks; Situation awareness; Reinforcement learning; Adaptive resonance theory; NEURAL NETWORK;

D O I：

10.1016/j.neunet.2009.03.013

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：316 / 325

页数：10

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