Synthesizing Stigmergy for Multi Agent Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4088)


In order to synthesize stigmergy a model needs to be created that allows a collective of agents to achieve global results through local interactions in some environment. This locality of interactions between the agents and between the agent and the environment allows for the distribution of the entire system without any centralization. Stigmergy is found among social insects in nature. These natural systems show remarkable flexibility, robustness and self-organisation. These characteristics are sort after in modern software systems. Utilizing stigmergy in an artificial system allows agents to interact with one another and with the general topology in a non-centralized manner, thus giving rise to a collective solution when solving of certain tasks. Even though the agents are localized their interaction with the stigmergy layer allows other agents to be affected by the interactions. The methology of mimicking stigmergy into a software system will be described and a description of the model used to synthesize stigmergy will be given. The potential utilization of stigmergy by software agents to interact with each other and to solve certain tasks collectively is also demonstrated.


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  1. 1.
    Bonabeau, E., Meyer, C.: Swarm Intelligence: A Whole New Way to Think About Business. Harvard Business Review. Harvard Business School Publishing Corporation (2001)Google Scholar
  2. 2.
    Sutherland, J.: Business Object and Component Architectures: Enterprise Application Integration Encounters Complex Adaptive Systems. In: HICSS-34 Outrigger Wailea Resort Maui (January 2001)Google Scholar
  3. 3.
    Sutherland, J., van den Heuvel, W.: Enterprise Application Integration Encounters Complex Adaptive Systems: A Business Object Perspective. In: 35th Annual Hawaii International Conference on System Sciences (HICSS 2002), January 2002, Big Island, Hawaii, vol. 9 (2002)Google Scholar
  4. 4.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence From Natural to Artifical Systems. Santa Fe Institute, Studies in the science of complexity. Oxford University Press, New York (1999)Google Scholar
  5. 5.
    Holland, J.H.: Hidden Order: How Adaption Builds Complexity. Helix Books (1995)Google Scholar
  6. 6.
    Montresor, A., Meling, H., Babaoğlu, Ö.: Towards Adaptive, Resilient and Self-Organizing Peer-to-Perr Systems. Technical Report UBLCS-2002-09, Dept. of Computer Science, University of Bologna (September 2002)Google Scholar
  7. 7.
    Montresor, A., Meling, H., Babaoğlu, Ö.: Toward Self-Organizing, Self-Repairing and Resilient Large-Scale Distributed Systems. Technical Report UBLCS-2002-10, Dept. of Computer Science, University of Bologna (September 2002)Google Scholar
  8. 8.
    Van Dyke Parunak, H., Brueckner, S., Sauter, J.: Digital pheromone mechanisms for coordination of unmanned vehicles. In: 1st International Joint Conference on Autonomous Agents and Multiagent Systems AAMAS 2002, pp. 449–450. ACM Press, New York (2003)Google Scholar
  9. 9.
    Valckenaers, P., Kollingbaum, M., Van Brussel, H., Bochmann, O., Zamfirescu, C.: The Design of Multi-Agent Coordination and Control Systems using Stigmergy. In: Proc. of the IWES 2001 Conference (2001),
  10. 10.
    Forgy, C.L.: Rete: A Fast Algorithm for the Many pattern/Many Object-Pattern Matching Problem. Artificial Intelligence 19(1), 17–37 (1982)CrossRefGoogle Scholar
  11. 11.
    JESS, The Java Expert System Shell, E.J. Friedman-Hill, Distributed Computing Systems, Sandia National Laboratories, Verion 5.0 (January 2000)Google Scholar
  12. 12.
  13. 13.
    Leonardi, L., Mamei, M., Zambonelli, F.: Co-Fields: Towards a Unified Model for Swarm Intelligence (2002),
  14. 14.
    Gruninger, M., Lee, J.: Ontology Applications and Design. Communication of the ACM 45(2), 39–41 (2002)CrossRefGoogle Scholar
  15. 15.
    Mizoguchi, R.: Ontology-Based Systematization of Functional Knowledge. In: Proceeding of the TMCE, April 2002, Wuhan, China, pp. 45–64 (2002)Google Scholar
  16. 16.
    O’Reilly, G.B., Ehlers, E.M.: Utilizing the Swarm Effect to Optimize the Frequency Assignment Problem, January 2006 (Submitted for publication, 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Academy of Information TechnologyUniversity of JohannesburgJohannesburgSouth Africa

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