Full Text:   <1190>

Summary:  <38>

CLC number: TP11

On-line Access: 2022-08-22

Received: 2021-09-02

Revision Accepted: 2021-10-03

Crosschecked: 2022-08-29

Cited: 0

Clicked: 2173

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Fei-Yue WANG

https://orcid.org/0000-0001-9185-3989

Jun Jason ZHANG

https://orcid.org/0000-0001-6908-2671

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.8 P.1142-1157

http://doi.org/10.1631/FITEE.2100418


Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems


Author(s):  Fei-Yue WANG, Jianbo GUO, Guangquan BU, Jun Jason ZHANG

Affiliation(s):  The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; more

Corresponding email(s):   jun.zhang.ee@whu.edu.cn

Key Words:  Complex systems, Human-machine knowledge automation, Parallel systems, Bulk power grid dispatch, Artificialintelligence, Internet of Minds (IoM)


Fei-Yue WANG, Jianbo GUO, Guangquan BU, Jun Jason ZHANG. Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1142-1157.

@article{title="Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems",
author="Fei-Yue WANG, Jianbo GUO, Guangquan BU, Jun Jason ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="8",
pages="1142-1157",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100418"
}

%0 Journal Article
%T Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems
%A Fei-Yue WANG
%A Jianbo GUO
%A Guangquan BU
%A Jun Jason ZHANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 8
%P 1142-1157
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100418

TY - JOUR
T1 - Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems
A1 - Fei-Yue WANG
A1 - Jianbo GUO
A1 - Guangquan BU
A1 - Jun Jason ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 8
SP - 1142
EP - 1157
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100418


Abstract: 
In this paper, we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation (HM-KA) as the technical mechanism of hybrid augmented intelligence (HAI) based complex system cognition, management, and control (CMC). We describe the historical development of complex system science and analyze the limitations of human intelligence and machine intelligence. The need for using human-machine HAI in complex systems is then explained in detail. The concept of “mutually trustworthy HM-KA” mechanism is proposed to tackle the CMC challenge, and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid dispatch. It is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

人机互信的知识自动化与混合增强智能:复杂系统认知管控机制及其应用

王飞跃1,郭剑波2,卜广全3,张俊4
1中国科学院自动化研究所复杂系统管理与控制国家重点实验室,中国北京市,100190
2中国国家电网有限公司,中国北京市,100031
3中国电力科学研究院有限公司,中国北京市,100192
4武汉大学电气与自动化学院,中国武汉市,430072
摘要:本文旨在阐述复杂系统认知、管理和控制中人机互信的混合增强智能和知识自动化机制与应用。本文从复杂系统研究的发展历程出发,通过对复杂系统的特性、人工智能科技、人机混合增强智能科技及其在复杂系统管控中的必要性阐述,分析了人类智能、机器智能在复杂系统管控中的优势与局限性,并提出"人机互信知识自动化"的概念。以电力系统大电网调控为背景,阐述了未来人机混合智能在大电网调度中可能的技术路径和应用基础,并以潮流校正控制为例,说明人机知识自动化任务流程的完成过程。通过本文内容的阐述,希望对基于人机混合增强智能的复杂系统管理和控制的理论方法提供一种新的机制和应用路径,并对社会典型复杂系统管控的数字化、智能化建设起到积极作用。

关键词:复杂系统;人机知识自动化;平行系统;大电网调度;人工智能;智联网

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Binney JJ, Dowrick NJ, Fisher AJ, et al., 1992. The Theory of Critical Phenomena: an Introduction to the Renormalization Group. Clarendon Press, Oxford, UK.

[2]Chen SY, Duan JJ, Bai YY, et al., 2021. Active power correction strategies based on deep reinforcement learning—Part II: a distributed solution for adaptability. CSEE J Power Energy Syst, early access.

[3]Dai YX, Chen QM, Gao TL, et al., 2021a. Deep reinforcement learning control policy extraction based on weighted oblique decision tree. Electr Power Inform Commun Technol, 19(11):17-23.

[4]Dai YX, Zhang J, Ji ZX, et al., 2021b. Intelligent diagnosis and auxiliary decision of power system secondary equipment based on functional defect text. Electr Power Autom Equip, 41(6):184-191.

[5]Gallagher R, Appenzeller T, 1999. Beyond reductionism. Science, 284(5411):79.

[6]Haken H, 1977. Synergetics. Phys Bull, 28(9):412-414.

[7]He HL, Guo JB, Song YT, et al., 2008. Risk based probabilistic security evaluation algorithm of bulk power system. 3rd Int Conf on Electric Utility Deregulation and Restructuring and Power Technologies, p.1231-1236.

[8]Hey T, Tansley S, Tolle K, 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond, USA.

[9]Holland JH, 1995. Hidden Order: How Adaptation Builds Complexity. Addison-Wesley, New York, USA.

[10]Liu XT, Liang BC, Liu L, et al., 2008. The Theory, Method & Technique for Complex System Modeling. Science Press, Beijing, China (in Chinese).

[11]Lorenz EN, 1963. Deterministic nonperiodic flow. J Atmos Sci, 20(2):130-141.

[12]Mandelbrot BB, 1989. Fractal geometry: what is it, and what does it do? Proc Roy Soc Lond A Math Phys Sci, 423(1864):3-16.

[13]Manoj BS, Chakraborty A, Singh R, 2018. Complex Networks: a Networking and Signal Processing Perspective. Prentice Hall, Boston, USA.

[14]Miller JH, Page SE, 2007. Complex Adaptive Systems: an Introduction to Computational Models of Social Life. Princeton University Press, Princeton, UK.

[15]Prigogine I, Lefever R, 1973. Theory of dissipative structures. In: Haken H (Ed.), Synergetics. Vieweg+Teubner Verlag, Wiesbaden, Germany, p.124-135.

[16]RTE-France, 2021. Grid2Op. https://github.com/rte-france/Grid2Op [Accessed on July 13, 2021].

[17]Sayama H, 2015. Introduction to the Modeling and Analysis of Complex Systems. Open SUNY Textbooks, New York, USA.

[18]Shannon CE, 1948. A mathematical theory of communication. Bell Syst Tech J, 27(3):379-423.

[19]Song YT, He HL, Zhang DX, et al., 2006. Prsobabilistic security evaluation of bulk power system considering cascading outages. Int Conf on Power System Technology, p.1-6.

[20]Thom R, 1975. Structural Stability and Morphogenesis. Addison-Wesley Benjamin, Inc., New York, USA.

[21]Thurner S, Hanel R, Klimek P, 2018. Introduction to the Theory of Complex Systems. Oxford University Press, Oxford, UK.

[22]Vadari S, 2012. Electric System Operations: Evolving to the Modern Grid. Artech House, Boston, USA.

[23]von Bertalanffy L, 1968. General System Theory. George Braziller Inc., New York, USA.

[24]Wang FY, 2004. Parallel system methods for management and control of complex systems. Contr Dec, 19(5):485-489, 514 (in Chinese).

[25]Wang FY, Chen JL, 2020. Intelligent Control Method and Application. China Science and Technology Press, Beijing, China (in Chinese).

[26]Wang FY, Zhang J, 2017. Internet of Minds: the concept, issues and platforms. Acta Autom Sin, 43(12):‍2061-2070 (in Chinese).

[27]Wang FY, Zhang J, Zhang J, et al., 2018. Industrial Internet of Minds: concept, technology and application. Acta Autom Sin, 44(9):1606-1617 (in Chinese).

[28]Wickens CD, Gordon SE, Liu YL, 1998. An Introduction to Human Factors Engineering. Longman, New York, USA.

[29]Wiener N, 1948. Cybernetics or Control and Communication in the Animal and the Machine. John Wiley & Sons Inc., New York, USA.

[30]Xu PD, Duan JJ, Zhang J, et al., 2021. Active power correction strategies based on deep reinforcement learning—Part I: a simulation-driven solution for robustness. CSEE J Power Energy Syst, early access.

[31]Zhang K, Zhang J, Xu PD, et al., 2021a. Explainable AI in deep reinforcement learning models for power system emergency control. IEEE Trans Comput Soc Syst, 9(2):419-427.

[32]Zhang K, Xu PD, Gao TL, et al., 2021b. A trustworthy framework of artificial intelligence for power grid dispatching systems. IEEE 1st Int Conf on Digital Twins and Parallel Intelligence, p.418-421.

[33]Zhang TY, Gao TL, Xu PD, et al., 2020. A review of AI and AI intelligence assessment. IEEE 4th Conf on Energy Internet and Energy System Integration, p.3039-3044.

[34]Zheng NN, Liu ZY, Ren PJ, et al., 2017. Hybrid-augmented intelligence: collaboration and cognition. Front Inform Technol Electron Eng, 18(2):153-179.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE