Dynamic Structuring in Cellular Self-Organizing Systems

  • Newsha Khani
  • Yan Jin


Conventional mechanical systems composed of various modules and parts are often inherently inadequate for dealing with unforeseeable changing situations. Taking advantage of the flexibility of multi-agent systems, a cellular self-organizing (CSO) systems approach has been proposed, in which mechanical cells or agents self-organize themselves as the environment and tasks change based on a set of rules. To enable CSO systems to deal with more realistic tasks, a two-field mechanism is introduced to describe task and agents complexities and to investigate how social rules among agents can influence CSO system performance with increasing task complexity. The simulation results of case studies based on the proposed mechanism provide insights into task-driven dynamic structures and their effect on the behavior, and consequently the function, of CSO systems.


Task Complexity Social Rule Task Environment Mechanical Cell Social Complexity 
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  1. 1.
    Ashby WR (1956) An introduction to cybernetics. Taylor & Francis, LondonGoogle Scholar
  2. 2.
    Zouein G, Chen C, Jin Y (2010) Create adaptive systems through ‘DNA’ guided cellular formation. Des Creat 2010:149Google Scholar
  3. 3.
    Chiang W, Jin Y (2011) Toward a meta-model of behavioral interaction for designing complex adaptive systems. IDETC2011-48821, 29–31 AugGoogle Scholar
  4. 4.
    Chen C, Jin Y (2011) A behavior based approach to cellular self-organizing sys. design. IDETC2011-48833, 29–31 Aug, Washington, DCGoogle Scholar
  5. 5.
    Simon HA (1962) The architecture of complexity. Proc Am Philos Soc 106:467–482Google Scholar
  6. 6.
    Williams EL (1981) Thermodynamics and the development of order. Creation research society booksGoogle Scholar
  7. 7.
    Ferguson S, Lewis K (2006) Effective development of reconfigurable systems using linear state-feedback control. AIAA J 44(4):868–878CrossRefGoogle Scholar
  8. 8.
    Martin MV, Ishii K (2002) Design for variety: developing standardized & modularized product platform architectures. Res Eng Design 13(4):213–235Google Scholar
  9. 9.
    Unsal C, Kilic H, Khosla P (2001) A modular self-reconfigurable bipartite robotic system: implementation and motion planning, Kluwer Autonom. Robots 10:23–40CrossRefGoogle Scholar
  10. 10.
    Yim M, Zhang Y, Duff D (2002) Modular robots. IEEE Spectrum 39(2):30–34CrossRefGoogle Scholar
  11. 11.
    Shen WM, Krivokon M, Chiu H, Everist J, Rubenstein M, Venkatesh J (2006) Multimode locomotion for reconfigurable robots. Auton Robot 20(2):165–177CrossRefGoogle Scholar
  12. 12.
    Horling B, Lesser V (2004) A survey of multi-agent organizational paradigms. Knowl Eng Rev 19(4):281–316CrossRefGoogle Scholar
  13. 13.
    Galbraith JR (1977) Organization design. Addison-Wesley, ReadingGoogle Scholar
  14. 14.
    Brooks CH, Durfee EH (2003) Congregation formation in multi-agent systems. Auton Agent Multi-Agent Syst 7(1):145–170CrossRefGoogle Scholar
  15. 15.
    Huberman B, Hogg T (1986) Complexity and adaptation. Physica D 22(3):376–384CrossRefMathSciNetGoogle Scholar
  16. 16.
    Wood RE (1986) Task complexity: definition of the construct. Organ Behav Hum Decis Process 37(1):60–82CrossRefGoogle Scholar
  17. 17.
    Campbell DJ (1988) Task complexity: a review and analysis. Acad Manag Rev 13(1):40–52Google Scholar
  18. 18.
    Gell-Mann M (1995) What is complexity? Complexity 1(1):16–19CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Bonchev D (2004) Complexity analysis of yeast proteome network. Chem Biodivers 1(2):312–326CrossRefGoogle Scholar
  20. 20.
    Wilensky U (2001). Modeling nature’s emergent patterns with multi-agent languages. Paper presented at Euro Logo 2001. Linz, AustriaGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of Southern CaliforniaLos AngelesUSA

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