logo

SCIENTIA SINICA Informationis, Volume 49, Issue 12: 1559-1571(2019) https://doi.org/10.1360/SSI-2019-0111

Artificial system architecture adjustment method based on multi-living agent

More info
  • ReceivedMay 30, 2019
  • AcceptedOct 23, 2019
  • PublishedDec 17, 2019

Abstract

In complex dynamic social environments with strong constraints and confrontation, a solidified artificial system architecture often cannot meet complex and varied service task requirements. This paper proposes an artificial system architecture adjustment method based on the multi-living agent (MLA) theroy by enhancing the administrators' management and control functions, that is, by replacing the primary multi-living agent or adding new multi-living agents. In this way, the self-organizing mechanism of the artificial system is changed and the principal and subordinate position of the opposite contradiction is changed. The livelihood degree of the system function is maintained or improved and the system function is realized. Specifically, in this paper we briefly review the MLA theory and then present the proposed artificial system architecture adjustment method. In the end, the effectiveness of the proposed method is verified by the air defense system against P2V-7 reconnaissance aircraft.


Funded by

国家自然科学基金(61421001)


References

[1] Qian X S, Yu J Y, Dai R W. A new discipline of science---the study of open complex giant system and its methodology. Chin J Nat, 1990, 1: 3--10+64. Google Scholar

[2] Wang Y. System Theory and Artificial System Design. Beijing: Beijing Institute of Technology Press, 2019. 132--158. Google Scholar

[3] Liu G, Wang Y, Zhou S Y. An outline of system theory based on artificial system. Syst Eng Electron, 1998, 7: 27--31. Google Scholar

[4] Thom R. Structural Stability and Morphogenesis. Sichuan: Sichuan Education Press, 1992. 1--438. Google Scholar

[5] Prochazka A, Gillard D, Bennett D J. Implications of positive feedback in the control of movement.. J NeuroPhysiol, 1997, 77: 3237-3251 CrossRef PubMed Google Scholar

[6] Ma L, Luo Y Q, Li S Y. Bifurcation analysis of a two-species diffusive model. Appl Math Lett, 2019, 96: 236-242 CrossRef Google Scholar

[7] Baker G, McRobie F A, Thompson J M T. Implications of chaos theory for engineering science. Proc Institution Mech Engineers Part C-J Mech Eng Sci, 1997, 211: 349-363 CrossRef Google Scholar

[8] Zhao H, Wu Q L. Application study of fractal theory in mechanical transmission. Chin J Mech Eng, 2016, 29: 871-879 CrossRef Google Scholar

[9] Ji F M, Luo L F. A hypercycle theory of proliferation of viruses and resistance to the viruses of transgenic plant.. J Theor Biol, 2000, 204: 453-465 CrossRef PubMed Google Scholar

[10] Jantsch E. The Self-organizing Universe. Oxford: Pergamon Press Ltd., 1980. 1--343. Google Scholar

[11] Haken H. Erfolgsgeheimnisse der natur synergetik: die lehre vom zusammenwirken. Shanghai: Shanghai Translation Publishing House, 2013. 50--60. Google Scholar

[12] Prigogine I, Stengers I. Order Out of Chaos. Shanghai: Shanghai Translation Publishing House, 2005. 70--80. Google Scholar

[13] Holland J H. Hidden Order: How Adaptation Builds Complexity. Shanghai: Shanghai Scientific and Technological Education Publishing House, 2011. 90--100. Google Scholar

[14] Wang Y. A novel method of constructing complex information system--multi-living agent method. Eng Sci, 2006, 8: 29--32+57. Google Scholar

[15] Wang Y, Tao R, Li B Z. Using the multi-living agent concept to investigate complex information systems. Sci Sin Inform, 2008, 38: 2020--2037. Google Scholar

[16] Wang Y, Tao R, Li B Z. Multi-living agent methods for the function enhancement of the information system. Sci Sin Inform, 2013, 43: 821-841 CrossRef Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1