Full Text:
<1545>
Summary: 
<97>
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: 2472
Citations: Bibtex RefMan EndNote GB/T7714
Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems
| ||||||||||||||
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.
[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.
ORCID: 
Open peer comments: Debate/Discuss/Question/Opinion
<1>