Developing Alternative Mechanisms for Multiagent Coordination

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2413)


Coordination is a key functionality and maybe the most challenging research issue in multiagent systems, and mechanisms for achieving coordinated behavior have been well-studied. One important observation has been that different mechanisms have correspondingly different performance characteristics, and that these can change dramatically in different environments (i.e., no one mechanism is best for all domains). A more recent observation is that one can describe possible mechanisms in a domain-independent way, as simple or complex responses to certain dependency relationships between the activities of different agents. Thus agent programmers can separate encoding agent domain actions from the solution to particular coordination problems that may arise. This paper explores the specification of a large range of coordination mechanisms for the common hard “enablement” (or “happens-before”) relationship between tasks at different agents. Essentially, a coordination mechanism can be described as a set of protocols possibly unique to the mechanism, and as an associated automatic re-writing of the specification of the domain-dependent task (expressed as an augmented HTN). This paper also presents a concrete implementation of this idea in the DECAF. A novel GPGP coordination component, between the planner and the scheduler, is developed in the DECAF agent architecture. An initial exploration of the separation of domain action from meta-level coordination actions for four simple coordination mechanisms is explained then.1,2


Multiagent System Coordination Mechanism Local Task Task Structure Agent Task 
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.
    Castelfranchi, C., Conte, R.: Distributed Artificial Intelligence and Social Science: Critical Issues. In Foundations of Distributed Artificial Intelligence, Chapter 20, 1996.Google Scholar
  2. 2.
    Chen, W., Decker, K.: Coordination Mechanisms for Dependency Relationships among Multiple Agents. Proceedings of the 1st International Joint Conference on Autonomous Agents and Mult-Agent Systems, Bologna, Italy, July, 2002.Google Scholar
  3. 3.
    Crowston, K., Osborn, C.: Modeling coordination from field experiments. Conference on Organizational Computing, Coordination and Collabor ation: Theories and Technologies for Computer-Supported Work, Austin, TX, 1989.Google Scholar
  4. 4.
    Davis, R., Smith, R.: Negotiation as a Metaphor for Distributed Problem Solving. Artificial Intelligence, 20:1 pp 63–109, 1983.CrossRefGoogle Scholar
  5. 5.
    Decker, K., Lesser, V.: Designing a Family of Coordination Algorithms. In Proceedings of the First International Conference on Multi-Agent Systems(ICMAS-95), San Francisco, June 1995.Google Scholar
  6. 6.
    Decker, K., Li, J.: Coordinating Mutually Exclusive Resources using GPGP. Autonomous Agents and Multi-Agent Systems, Volume 3, 2000.Google Scholar
  7. 7.
    Dellarocas, C., Klein, M.: An Experimental Evaluation of Domain-Independent Fault Handling Services in Open Multi-Agent Systems. In Proceedings of ICMAS’00, Boston, MA, USA, 2000.Google Scholar
  8. 8.
    Durfee, E., Lesser, V.: Partial Global Planning: A Coordination Framework for Distributed Hypothesis Formation. IEEE Transactions on Systems, Man and Cybernetics, 21(5), 1167–1183, September/October 1991.Google Scholar
  9. 9.
    Graham, J., Decker, K.: Towards a Distributed, Environment-Centered Agent Framework. In Intelligent Agents IV, Agent Theories, Architectures, and Languages, Springer-Verlag, 2000.Google Scholar
  10. 10.
    Graham, J.: Real-time Scheduling in Multi-agent Systems. University of Delaware, 2001.Google Scholar
  11. 11.
    Hogg, T., Huberman, B.: Controlling chaos in distributed systems. IEEE Transactions on Systems, Man, and Cybernetics, 21(6):1325–1332, 1991.CrossRefGoogle Scholar
  12. 12.
    Jennings, N.; Commitments and conventions: The foundation of coordination in multi-agent systems. The Knowledge Engineering Review, 8(3):223–250, 1993.CrossRefGoogle Scholar
  13. 13.
    Decker, K., Sycara, K.: Intelligent Adaptive Information Agents. In Journal of Intelligent Information System, 9, 239–260, 1997.CrossRefGoogle Scholar
  14. 14.
    Rustogi, S., Singh, M.: Be Patient and Tolerate Imprecision: How Autonomous Agents can Coordinate Effectively. In Proceedings of IJCAI’99, Stockholm, Sweden, August, 1999.Google Scholar
  15. 15.
    Sen, S., Roychoudhury, S., Arora, N.: Effect of local information on group behavior. In Proceedings of the International Conference on MAS, page 315–321, 1996.Google Scholar
  16. 16.
    So, Y., Durfee, E.: Designing Organizations for Computational Agent. In M.J. Pritula, K.M. Carley, and L. Gasser, editors, Simulating Organizations, page 47–64. AAAI press, 1998.Google Scholar
  17. 17.
    Tambe, M.: Teamwork in real-world, dynamic environments. In International conference on multi-agent systems (ICMAS96).Google Scholar
  18. 18.
    Wagner, T., Garvey, A., Lesser, V.: Complex Goal Criteria and its Application in Design-To-Criteria Scheduling. In Proceedings of the Fourteenth National Conference on Artificial Intelligence.Google Scholar
  19. 19.
    Weiss, G.: Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence. P88–P92, MIT Press, 1999.Google Scholar
  20. 20.
    Willmott, S., Faltings, B.: The Benefits of Environment Adaptive Organizations for Agent Coordination and Network Routing Problems. In Proceedings of IJCAI⊃9, Stockholm, Sweden, August, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  1. 1.University of DelawareNewarkUSA

Personalised recommendations