A Systemic Approach to the Validation of Self–Organizing Dynamics within MAS

  • Jan Sudeikat
  • Wolfgang Renz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5386)


Conceiving applications as sets of autonomous agents is a prominent approach to the construction of complex distributed systems. Particularly attractive are decentralized application designs that enable adaptive, robust and scalable applications by allowing agents to self–organize. Tools to the construction of self–organizing MAS, e.g. decentralized coordination strategies, catch increasing attention in MAS research. However, their purposeful utilization challenges current development practices. The intended non–linear macroscopic dynamics hinder top–down designs on the drawing board and corresponding development procedures rely on sequences of manual system simulation. In order to stimulate methodical development and facilitate the validation of complex MAS by simulation, we present a systemic approach to the qualitative validation of macroscopic MAS dynamics. Describing MAS as dynamical systems enables developers to formulate hypotheses on the intended macroscopic MAS behaviors that guide system simulations. We discuss and exemplify how to (1) derive systemic models as well as hypotheses from MAS designs, (2) infer appropriate simulation settings to their validation and (3) interpret the obtained results. In addition, work in progress on the automation of both system simulations and their interpretation is outlined.


Intrusion Detection Intrusion Detection System Organize Dynamics Coordination Strategy Causal Loop Diagram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jennings, N.R.: Building complex, distributed systems: the case for an agent-based approach. Comms. of the ACM 44(4), 35–41 (2001)CrossRefGoogle Scholar
  2. 2.
    Serugendo, G.D.M., Gleizes, M.P., Karageorgos, A.: Self–organisation and emergence in mas: An overview. Informatica 30, 45–54 (2006)zbMATHGoogle Scholar
  3. 3.
    Parunak, H.V.D., Brueckner, S.: Engineering swarming systems. In: Methodologies and Software Engineering for Agent Systems, pp. 341–376. Kluwer, Dordrecht (2004)CrossRefGoogle Scholar
  4. 4.
    Sudeikat, J., Renz, W.: Building Complex Adaptive Systems: On Engineering Self–Organizing Multi–Agent Systems. In: Applications of Complex Adaptive Systems, pp. 229–256. IGI Global (2008)Google Scholar
  5. 5.
    Edmonds, B.: Using the experimental method to produce reliable self-organised systems. In: Brueckner, S.A., Di Marzo Serugendo, G., Karageorgos, A., Nagpal, R. (eds.) ESOA 2005. LNCS (LNAI), vol. 3464, pp. 84–99. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Sudeikat, J., Braubach, L., Pokahr, A., Lamersdorf, W., Renz, W.: Validation of bdi agents. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) PROMAS 2006. LNCS (LNAI), vol. 4411, pp. 185–200. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Mamei, M., Menezes, R., Tolksdorf, R., Zambonelli, F.: Case studies for self-organization in computer science. J. Syst. Archit. 52(8), 443–460 (2006)CrossRefGoogle Scholar
  8. 8.
    DeWolf, T., Holvoet, T.: Decentralised coordination mechanisms as design patterns for self-organising emergent applications. In: Proceedings of the Fourth International Workshop on Engineering Self-Organising Applications, pp. 40–61 (2006)Google Scholar
  9. 9.
    DeWolf, T., Holvoet, T.: Using uml 2 activity diagrams to design information flows and feedback-loops in self-organising emergent systems. In: Proceedings of the Second International Workshop on Engineering Emergence in Decentralised Autonomic Systems, EEDAS 2007 (2007)Google Scholar
  10. 10.
    Mamei, M., Zambonelli, F., Leonardi, L.: Co–fields: A physically inspired approach to motion coordination. IEEE Pervasive Computing 03(2), 52–61 (2004)CrossRefGoogle Scholar
  11. 11.
    DeWolf, T., Holvoet, T.: A taxonomy for self-* properties in decentralised autonomic computing. In: Autonomic Computing: Concepts, Infrastructure, and Applications (2006)Google Scholar
  12. 12.
    Sudeikat, J., Renz, W.: On the redesign of self–organizing multi–agent systems. International Transactions on Systems Science and Applications 2(1), 81–89 (2006)Google Scholar
  13. 13.
    Sudeikat, J., Renz, W.: Toward systemic mas development: Enforcing decentralized self–organization by composition and refinement of archetype dynamics. In: Weyns, D., Brueckner, S.A., Demazeau, Y. (eds.) EEMMAS 2007. LNCS, vol. 5049, pp. 39–57. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    DeWolf, T., Holvoet, T.: Towards a methodolgy for engineering self-organising emergent systems. In: Proceedings of the International Conference on Self-Organization and Adaptation of Multi-agent and Grid Systems (2005)Google Scholar
  15. 15.
    Mao, X., Yu, E.: Organizational and social concepts in agent oriented software engineering. In: Odell, J.J., Giorgini, P., Müller, J.P. (eds.) AOSE 2004. LNCS, vol. 3382, pp. 1–15. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Sterman, J.D.: Business Dynamics - Systems Thinking an Modeling for a Complex World. McGraw-Hill, New York (2000)Google Scholar
  17. 17.
    Odell, J.J., Parunak, H.V.D., Brueckner, S., Sauter, J.: Temporal aspects of dynamic role assignment. In: Giorgini, P., Müller, J.P., Odell, J.J. (eds.) AOSE 2003. LNCS, vol. 2935, pp. 201–213. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Odell, J., Parunak, H.V.D., Bauer, B.: Extending uml for agents. In: Proceedings of the Agent-Oriented Information Systems Workshop at the 17th National conference on Artificial Intelligence (2000)Google Scholar
  19. 19.
    Ferber, J., Gutknecht, O., Michel, F.: From agents to organizations: An organizational view of multi-agent systems. In: Giorgini, P., Müller, J.P., Odell, J.J. (eds.) AOSE 2003. LNCS, vol. 2935, pp. 214–230. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Lerman, K., Galstyan, A.: Automatically modeling group behavior of simple agents. In: Agent Modeling Workshop, AAMAS 2004 (2004)Google Scholar
  21. 21.
    Mogul, J.C.: Emergent (mis)behavior vs. complex software systems. Technical Report HPL-2006-2, HP Laboratories Palo Alto (2005)Google Scholar
  22. 22.
    Sudeikat, J., Renz, W.: Monitoring group behavior in goal–directed agents using co–efficient plan observation. In: Padgham, L., Zambonelli, F. (eds.) AOSE VII / AOSE 2006. LNCS, vol. 4405, pp. 174–189. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Sudeikat, J., Renz, W.: On expressing and validating requirements for the adaptivity of self–organizing multi–agent systems. System and Information Sciences Notes 2(1), 14–19 (2007)Google Scholar
  24. 24.
    Axtell, R., Axelrod, R., Epstein, J.M., Cohen, M.D.: Aligning simulation models: A case study and results. Computational & Mathematical Organization Theory 1(2), 123–141 (1996)CrossRefGoogle Scholar
  25. 25.
    Wilson, W.: Resolving discrepancies between deterministic population models and individual–based simulations. The American Naturalist 151, 116–134 (1998)Google Scholar
  26. 26.
    Brueckner, S.A., Parunak, H.V.D.: Resource-aware exploration of the emergent dynamics of simulated systems. In: AAMAS 2003: Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pp. 781–788. ACM Press, New York (2003)CrossRefGoogle Scholar
  27. 27.
    Liu, J., Tsui, K.: Toward nature-inspired computing. Commun. ACM 49(10), 59–64 (2006)CrossRefGoogle Scholar
  28. 28.
    Padgham, L., Winikoff, M.: Developing Intelligent Agent Systems: A Practical Guide. John Wiley and Sons, Chichester (2004)CrossRefzbMATHGoogle Scholar
  29. 29.
    Gardelli, L., Viroli, M., Omicini, A.: On the role of simulations in engineering self-organising mas: The case of an intrusion detection system in tucson. In: Brueckner, S.A., Di Marzo Serugendo, G., Hales, D., Zambonelli, F. (eds.) ESOA 2005. LNCS (LNAI), vol. 3910, pp. 153–166. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  30. 30.
    Blossey, R., Cardelli, L., Phillips, A.: A compositional approach to the stochastic dynamics of gene networks. In: Priami, C., Cardelli, L., Emmott, S. (eds.) Transactions on Computational Systems Biology IV. LNCS (LNBI), vol. 3939, pp. 99–122. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  31. 31.
    Priami, C.: Stochastic π–calculus. Computer Journal 6, 578–589 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jan Sudeikat
    • 1
  • Wolfgang Renz
    • 1
  1. 1.Multimedia Systems LaboratoryHamburg University of Applied SciencesHamburgGermany

Personalised recommendations