Research on New Distributed Solution Method of Complex System Based on MAS

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 107)


Generalized decision function-based decomposition criterion in multi-agent systems and multi-agent task specification decomposition features are put forward in this paper. It is pointed out that problem of tasks decomposition in multi-agent systems is equivalent to that forming a multiply sectioned Bayesian (MSBN) related to information agent. The relation between d-cutset of MSBN and decomposition criterion is proved, and decomposition method of MSBN related to multi-agent task specification is put forward. Feasibility on the method put forward is verified with an example in the end.


Distribution computation Information agent Generalized decision function Task specification decomposition Bayesian network 



This work is supported by the Natural Science Foundation of Hunan Province of China No. 10JJ5064, the Society Science Foundation of Hunan Province of China No. 07YBB239, the Sci. and Technology Plan Project of China under of Hunan Grant No.2009GK2002, and the Technology Innovation Foundation under Science and Technology Ministry Grant No. 09C26214301947.


  1. 1.
    Polkowski L, Skwron A (1996) Rough metrology: a new paradigm for approximate reasoning, J. Int J Approx Reason 15(4):333–365CrossRefMATHGoogle Scholar
  2. 2.
    Jiang WJ, Wang P (2006) Research on distributed solution and correspond consequence of complex system based on MAS [J]. J Comput Res Dev 43(9):1615–1623CrossRefMathSciNetGoogle Scholar
  3. 3.
    Sleazk D (2006) Approximate reducts in decision tables [A]. IMPU-96 Information processing and management of uncertainty on knowledge-based systems [C], Granada, Spain, 1–5(3):1159–1164Google Scholar
  4. 4.
    Pearl J (1988) A probabilistic reasoning in intelligence system: networks of plausible inference [M]. Morgan Kaufmann, San MoteoGoogle Scholar
  5. 5.
    Xiang Y (1996) A probabilistic frame work for cooperative multi-agent distributed interpretation and optimization of communication [J]. Artif Intell 87(1–2):295–342CrossRefGoogle Scholar
  6. 6.
    Wooldridge M, Jenning NR (1995) Intelligent agent: theory and practice [J]. Knowl Eng Rev 10(2):115–152CrossRefGoogle Scholar
  7. 7.
    Xiang Y (2008) Bayesian network repository [EB/OD]. info/yxiang/index.html
  8. 8.
    Xu K, Wang Y, Wu C (2007) Test technology and application of model of chain of services of Grid [J]. Sci China, Vol E, 37(4):467–485Google Scholar
  9. 9.
    Weicai Z, Jin L, Mingzhi X et al (2004) A multi agent genetic algorithm for global numerical optimization [J]. IEEE Trans Syst Man Cybern 34(2):1128–1141Google Scholar
  10. 10.
    Busoniu L, Babuska R, Schutter BD (2008) A comprehensive survey of multiagent reinforcement learning. IEEE Trans Syst Man Cybern-Part C Appl Rev 38(2):156–172CrossRefGoogle Scholar
  11. 11.
    Gou Y, Huan JZ, Rong H (2005) Adaptive grid job allocation with genetic algorithm. In: Future Genre Comp Sys. Elsevier Press, London 21:151–161Google Scholar
  12. 12.
    Matsui M (1993) A generalized model of convey-serviced production station (CSPS). J Jpn Ind Manag Assoc 44(1):25–32Google Scholar
  13. 13.
    Bredin J, Kotz D, Rus D, Maheswaran RT, Imer C, Basar T (2003) Computational markets to regulate mobile-agent systems [J]. Auton Agents Multi-Agent Syst 6(3):235–263CrossRefGoogle Scholar
  14. 14.
    Nakase N, Yamada T, Matsui M (2002) A management design approach to a simple flexible assembly system. Int J Prod Econ 76:281–292CrossRefGoogle Scholar
  15. 15.
    Jiang WJ, Wang P, Lianmei Z (2009) Research on grid resource scheduling algorithm based on MAS cooperative bidding game. Chin Sci F 52(8):1302–1320CrossRefMATHGoogle Scholar
  16. 16.
    Jiang WJ, Wang P (2006) Research on distributed solution and correspond consequence of complex system based on MAS [J]. J Comput Res Dev 43(9):1615–1623CrossRefMathSciNetGoogle Scholar
  17. 17.
    Jie C, Greiner R, Kelly J et al (2002) Learning Bayesian network from data: an information-theory based approach [J]. Artif Intell 137:43–90CrossRefMATHGoogle Scholar
  18. 18.
    Choi SPM, Liu J (2007) A genetic agent-based negotiation system [J]. Comput Netw (37):195–204Google Scholar
  19. 19.
    Guiquan L, Xiaoping C, Yan F et al (2007) A formal model of multi-agent cooperative systems. J Comput 24(5):529–535Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.School of Computer and Electronic EngineeringHunan University of CommerceChangshaChina
  2. 2.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina

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