How to learn from the resilience of Human-Machine Systems?

被引：37
|
作者：
Ouedraogo, Kiswendsida Abel [1 ,2 ,3 ]
Enjalbert, Simon [1 ,2 ,3 ]
Vanderhaegen, Frederic [1 ,2 ,3 ]
机构：
[1] Univ Lille Nord France, F-59000 Lille, France
[2] UVHC, LAMIH, F-59313 Le Mt Houy, Valenciennes, France
[3] CNRS, FRE 3304, F-59313 Valenciennes, France
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2013年 / 26卷 / 01期
关键词：
Human-Machine Systems; Resilience; Learning process; Feedback/feedforward control; ERROR-RESILIENCE; ORGANIZATION; COOPERATION; DESIGN; MODEL;
D O I：
10.1016/j.engappai.2012.03.007
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a functional architecture to learn from resilience. First, it defines the concept of resilience applied to Human-Machine System (HMS) in terms of safety management for perturbations and proposes some indicators to assess this resilience. Local and global indicators for evaluating human-machine resilience are used for several criteria. A multi-criteria resilience approach is then developed in order to monitor the evolution of local and global resilience. The resilience indicators are the possible inputs of a learning system that is capable of producing several outputs, such as predictions of the possible evolutions of the system's resilience and possible alternatives for human operators to control resilience. Our system has a feedback-feedforward architecture and is capable of learning from the resilience indicators. A practical example is explained in detail to illustrate the feasibility of such prediction. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：24 / 34
页数：11
相关论文
共 50 条
  • [21] Distributed Human-Machine Systems: Progress and Prospects
    Bradshaw, Jeffrey M.
    ACTIVE MEDIA TECHNOLOGY, PROCEEDINGS, 2009, 5820 : 2 - 2
  • [22] INTEGRATED HUMAN-MACHINE INTELLIGENCE IN SPACE SYSTEMS
    BOY, GA
    ACTA ASTRONAUTICA, 1992, 27 : 175 - 183
  • [23] Conceptualizing hybrid human-machine systems and interaction
    Buxbaum-Conradi, Sonja
    Redlich, Tobias
    Branding, Jan-Hauke
    PROCEEDINGS OF THE 49TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS 2016), 2016, : 551 - 559
  • [24] Graphical Human-Machine Interface for QB Systems
    Jasinski, M.
    Nawrat, A.
    HUMAN-COMPUTER SYSTEMS INTERACTION: BACKGROUNDS AND APPLICATIONS, 2009, 60 : 407 - 417
  • [25] Human-machine collaborative systems for microsurgical applications
    Kragic, D
    Marayong, P
    Li, M
    Okamura, AM
    Hager, GA
    INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2005, 24 (09): : 731 - 741
  • [26] Situation awareness in human-machine interactive systems
    Ziemke, Tom
    Schaefer, Kristin E.
    Endsley, Mica
    COGNITIVE SYSTEMS RESEARCH, 2017, 46 : 1 - 2
  • [27] Speech technology and systems in human-machine communication
    Deng, L
    Wang, KS
    Chou, W
    IEEE SIGNAL PROCESSING MAGAZINE, 2005, 22 (05) : 12 - +
  • [28] Automation and problem solving in human-machine systems
    Sage, AP
    HUMAN INTERACTION WITH COMPLEX SYSTEMS: CONCEPTUAL PRINCIPLES AND DESIGN PRACTICE, 1996, 372 : 385 - 386
  • [29] A Framework to Guide the Assessment of Human-Machine Systems
    Stowers, Kimberly
    Oglesby, James
    Sonesh, Shirley
    Leyva, Kevin
    Iwig, Chelsea
    Salas, Eduardo
    HUMAN FACTORS, 2017, 59 (02) : 172 - 188
  • [30] Toward Further Innovation in Human-Machine Systems
    Tanaka, Yoshiyuki
    2016 IEEE 9TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (IWCIA), 2016, : 1 - 1
12345