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.
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.
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References
Binney JJ, Dowrick NJ, Fisher AJ, et al., 1992. The Theory of Critical Phenomena: an Introduction to the Renormalization Group. Clarendon Press, Oxford, UK.
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. https://doi.org/10.17775/CSEEJPES.2020.07070
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.
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. https://doi.org/10.16081/j.epae.202106006
Gallagher R, Appenzeller T, 1999. Beyond reductionism. Science, 284(5411):79. https://doi.org/10.1126/science.284.5411.79
Haken H, 1977. Synergetics. Phys Bull, 28(9):412–414. https://doi.org/10.1088/0031-9112/28/9/027
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. https://doi.org/10.1109/DRPT.2008.4523595
Hey T, Tansley S, Tolle K, 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond, USA.
Holland JH, 1995. Hidden Order: How Adaptation Builds Complexity. Addison-Wesley, New York, USA.
Liu XT, Liang BC, Liu L, et al., 2008. The Theory, Method & Technique for Complex System Modeling. Science Press, Beijing, China (in Chinese).
Lorenz EN, 1963. Deterministic nonperiodic flow. J Atmos Sci, 20(2):130–141.
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. https://doi.org/10.1098/rspa.1989.0038
Manoj BS, Chakraborty A, Singh R, 2018. Complex Networks: a Networking and Signal Processing Perspective. Prentice Hall, Boston, USA.
Miller JH, Page SE, 2007. Complex Adaptive Systems: an Introduction to Computational Models of Social Life. Princeton University Press, Princeton, UK.
Prigogine I, Lefever R, 1973. Theory of dissipative structures. In: Haken H (Ed.), Synergetics. Vieweg+Teubner Verlag, Wiesbaden, Germany, p.124–135. https://doi.org/10.1007/978-3-663-01511-6_10
RTE-France, 2021. Grid2Op. https://github.com/rte-france/Grid2Op [Accessed on July 13, 2021].
Sayama H, 2015. Introduction to the Modeling and Analysis of Complex Systems. Open SUNY Textbooks, New York, USA.
Shannon CE, 1948. A mathematical theory of communication. Bell Syst Tech J, 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
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. https://doi.org/10.1109/ICPST.2006.321614
Thom R, 1975. Structural Stability and Morphogenesis. Addison-Wesley Benjamin, Inc., New York, USA.
Thurner S, Hanel R, Klimek P, 2018. Introduction to the Theory of Complex Systems. Oxford University Press, Oxford, UK.
Vadari S, 2012. Electric System Operations: Evolving to the Modern Grid. Artech House, Boston, USA.
von Bertalanffy L, 1968. General System Theory. George Braziller Inc., New York, USA.
Wang FY, 2004. Parallel system methods for management and control of complex systems. Contr Dec, 19(5): 485–489, 514 (in Chinese). https://doi.org/10.3321/j.issn:1001-0920.2004.05.002
Wang FY, Chen JL, 2020. Intelligent Control Method and Application. China Science and Technology Press, Beijing, China (in Chinese).
Wang FY, Zhang J, 2017. Internet of Minds: the concept, issues and platforms. Acta Autom Sin, 43(12):2061–2070 (in Chinese). https://doi.org/10.16383/j.aas.2017.y000006
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). https://doi.org/10.16383/j.aas.2018.y000004
Wickens CD, Gordon SE, Liu YL, 1998. An Introduction to Human Factors Engineering. Longman, New York, USA.
Wiener N, 1948. Cybernetics or Control and Communication in the Animal and the Machine. John Wiley & Sons Inc., New York, USA.
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. https://doi.org/10.17775/CSEEJPES.2020.07090
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. https://doi.org/10.1109/TCSS.2021.3096824
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.
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.
Zheng NN, Liu ZY, Ren PJ, et al., 2017. Hybrid-augmented intelligence: collaboration and cognition. Front Inform Technol Electron Eng, 18(2): 153–179. https://doi.org/10.1631/FITEE.1700053
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Fei-Yue WANG and Jianbo GUO designed the research. Guangquan BU and Jun Jason ZHANG conducted the analysis. Guangquan BU and Jun Jason ZHANG drafted the paper. Fei-Yue WANG and Jianbo GUO revised and finalized the paper.
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Fei-Yue WANG, Jianbo GUO, Guangquan BU, and Jun Jason ZHANG declare that they have no conflict of interest.
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Project supported by the National Key R&D Program of China (No. 2018AAA0101504) and the Science and Technology Project of the State Grid Corporation of China: Fundamental Theory of Human in-the-Loop Hybrid-Augmented Intelligence for Power Grid Dispatch and Control
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Wang, FY., Guo, J., Bu, G. et al. Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems. Front Inform Technol Electron Eng 23, 1142–1157 (2022). https://doi.org/10.1631/FITEE.2100418
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DOI: https://doi.org/10.1631/FITEE.2100418
Key words
- Complex systems
- Human-machine knowledge automation
- Parallel systems
- Bulk power grid dispatch
- Artificial intelligence
- Internet of Minds (IoM)