Principled Monitoring of Distributed Agents for Detection of Coordination Failure

  • Brett Browning
  • Gal A. Kaminka
  • Manuela M. Veloso


There is a very rich variety of systems of autonomous agents, be it software or robotic agents. In particular, multi-agent systems can include agents that may be part of a team and need to coordinate their actions during their distributed task execution. This coordination requires an agent to observe, i.e., to monitor, the other agents in order to detect a possible coordination failure of the team. Several researchers have addressed the problem of monitoring for single or multiple agent systems and have contributed successful, but mainly application-specific, approaches. In this paper, we aim at contributing a unifying, domain-independent statement of the distributed multi-agent monitoring problem. We define the problem in terms of a pre-defined desirable joint state and an observation-state mapping. Given a concrete joint observation during execution, we show how an agent can detect a possible coordination failure by processing the observation-state mapping and the desirable joint state. To illustrate the generality of our formalism, one of the main contributions of the paper, we represent several previously studied examples within our formalism. We note that basic failure detection algorithms can be computationally expensive. We further contribute an efficient method for failure detection that builds upon an off-line compilation of the principled relations introduced. We show empirical results that demonstrate this effectiveness.


Failure Detection Joint State Defense Advance Research Project Agency Coordination Failure Defense Advance Research Project Agency 
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.


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  1. 1.
    Milind Tambe, W. Lewis Johnson, Randy Jones, Frank Koss, John E. Laird, Paul S. Rosenbloom, and Karl Schwamb. Intelligent agents for interactive simulation environments. AI Magazine, 16(1), Spring 1995.Google Scholar
  2. 2.
    Marcus James Huber and Tedd Hadley. Multiple roles, multiple teams, dynamic environment: Autonomous netrek agents. In W. Lewis Johnson, editor, Proceedings of the International Conference on Autonomous Agents, pages 332–339, Marina del Rey, CA, 1997. ACM Press.Google Scholar
  3. 3.
    Hiroaki Kitano, Milind Tambe, Peter Stone, Manuela Veloso, Silvia Coradeschi, E. Os-awa, H. Matsubara, Itsuki Noda, and M. Asada. The RoboCup synthetic agent challenge ’97. In Proceedings of the International Joint Conference on Artificial Intelligence, Nagoya, Japan, 1997.Google Scholar
  4. 4.
    Lynne E. Parker. ALLIANCE: An architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automation, 14(2):220–240, April 1998.CrossRefGoogle Scholar
  5. 5.
    Tucker Balch. Behavioral Diversity in Learning Robot Teams. PhD thesis, Georgia Institute of Technology, 1998.Google Scholar
  6. 6.
    Maja J. Mataric. Interaction and Intelligent Behavior. PhD thesis, Massachusetts Institute of Technology, 1994.Google Scholar
  7. 7.
    Yasuo Kuniyoshi, Sebastien Rougeaux, Makoto Ishii, Nobuyuki Kita, Shigeyuki Sakane, and Masayoshi Kakikura. Cooperation by observation—the framework and the basic task patterns. In the IEEE International Conference on Robotics and Automation, pages 767–773, San-Diego, CA, May 1994. IEEE Computer Society Press.Google Scholar
  8. 8.
    Gal A. Kaminka and Milind Tambe. Robust multi-agent teams via socially-attentive monitoring. Journal of Artificial Intelligence Research, 12:105–147, 2000.MATHGoogle Scholar
  9. 9.
    Richard Washington. Markov tracking for agent coordination. In Proceedings of the International Conference on Autonomous Agents, pages 70–77, Minneapolis/St. Paul, MN, 1998. ACM Press.Google Scholar
  10. 10.
    Milind Tambe. Tracking dynamic team activity. In Proceedings of the National Conference on Artificial Intelligence (AAAI), August 1996.Google Scholar
  11. 11.
    Milind Tambe. Towards flexible teamwork. Journal of Artificial Intelligence Research, 7:83–124, 1997.Google Scholar

Copyright information

© Springer-Verlag Tokyo 2002

Authors and Affiliations

  • Brett Browning
    • 1
  • Gal A. Kaminka
    • 1
  • Manuela M. Veloso
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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