Local Distributed Agent Matchmaking

  • Elth Ogston
  • Stamatis Vassiliadis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2172)


This paper explores through simulation an abstract model of distributed matchmaking within multi-agent systems. We show that under certain conditions agents can find matches for cooperative tasks without the help of a predefined organization or central facilitator. We achieve this by having each agent search for partners among a small changing set of neighbors. We work with a system where agents look for any one of a number of identical task matches, and where the number of categories of tasks can be as large as 100. Agents dynamically form clusters 10 to 100 agents in size within which agents cooperate by exchanging addresses of non-matching neighbors. We find that control of these clusters cannot be easily distributed, but that distribution in the system as a whole can be maintained by limiting cluster size. We further show that in a dynamic system where tasks end and clusters change matchmaking can continue indefinitely organizing into new sets of clusters, as long as some agents are willing to be flexible and abandon tasks they cannot find matches for. We show that in this case unmatched tasks can have a probability as low as.00005 of being changed per turn.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Awerbuch, B., Peleg, D.: Online Tracking of Mobile Users. Jounal of the ACM, 42(5), (1995) 1021–1058 69zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Decker, K., Sycara, K., Williamson, M.: Middle-Agents for the Internet. Proceedings of the 15th International Joint Conference on Artificial Intelligence. (1997)578–583 69Google Scholar
  3. 3.
    Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, English ed., Addison-Wesley, Edinburgh, (1999) 67Google Scholar
  4. 4.
    Kuokka, D., Harada, L.: Matchmaking for Information Agents. Proceedings of the 14th International Joint Conference on Artificial Intelligence. (1995) 672–678 69Google Scholar
  5. 5.
    Mullender, S., Vitányi, P.: Distributed MatchMaking. Algorithmica, 3, (1988) 367–391 69zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Ogston, E., Vassiliadis, S.: Matchmaking Among Minimal Agents Without a Facilitator. Proceedings of the 5th International Conference on Autonomous Agents.(2001)608–615 68, 71Google Scholar
  7. 7.
    Shehory, O,: A Scalable Agent Location Mechanism. Lecture Notes in Artificial Intelligence, Intelligent Agents VI, M. Wooldridge and Y. Lesperance (Eds.). (1999) 162–17 69Google Scholar
  8. 8.
    Shehory, O., Kraus, S.: Methods for Task Allocation via Agent Coalition Formation. Artificial Intelligence, 101(1-2), (1998) 165–200 70zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Smith, R.: The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. IEEE Transactions On Computers, 29(12). (1980) 1104–1113 69CrossRefGoogle Scholar
  10. 10.
    Sycara, K., Lu, J., Klusch, M., Widoff, S.: Matchmaking among Heterogeneous Agents on the Internet. Proceedings AAAI Spring Symposium on Intelligent Agents in Cyberspace. (1999) 69Google Scholar
  11. 11.
    Vulkan, N., Jennings, N.: Efficient Mechanisms for the Supply of Services in Multi-Agent Environments. International Journal of Decision Support Systems, 28(1-2) (2000) 5–19 69CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Elth Ogston
    • 1
  • Stamatis Vassiliadis
    • 1
  1. 1.Computer Engineering Laboratory, ITSTU DelftDelftThe Netherlands

Personalised recommendations