When Agents Communicate Hypotheses in Critical Situations

  • Gauvain Bourgne
  • Nicolas Maudet
  • Suzanne Pinson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4327)


This paper discusses the problem of efficient propagation of uncertain information in dynamic environments and critical situations. When a number of (distributed) agents have only partial access to information, the explanation(s) and conclusion(s) they can draw from their observations are inevitably uncertain. In this context, the efficient propagation of information is concerned with two interrelated aspects: spreading the information as quickly as possible, and refining the hypotheses at the same time. We describe a formal framework designed to investigate this class of problem, and we report on preliminary results and experiments using the described theory.


Critical Situation Reputation System Interaction Protocol Communication Step Ground Instance 
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.
    Aspnes, J., Hurwood, W.: Spreading rumors rapidly despite an adversary. In: Proc. 15th ACM Symposium on Principles of Distributed Computing, pp. 143–151 (1996)Google Scholar
  2. 2.
    Bailey, N.: The Mathematical Theory of Infectious Diseases. Charles Griffin and Company, London (1975)zbMATHGoogle Scholar
  3. 3.
    Birman, K., Hayden, M., Ozkasap, O., Xiao, Z., Budiu, M., Minsky, Y.: Bimodal multicast. ACM Transactions on Computer Systems 17(2) (1999)Google Scholar
  4. 4.
    Braginsky, D., Estrin, D.: Rumor routing algorithm for sensor networks. In: Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications (2002)Google Scholar
  5. 5.
    Buchegger, S., Le Boudec, J.: The effect of rumor spreading in reputation systems for mobile ad-hoc networks. In: Proceedings of Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (2003) Google Scholar
  6. 6.
    Chlebus, B., Kowalski, D.: Gossiping to reach consensus. In: Proceedings of the 14th ACM Symp. on Parallel Algorithms and Architectures, pp. 220–229 (2002)Google Scholar
  7. 7.
    Cuenca-Acuna, F.M., Peery, C., Martin, R.P., Nguyen, T.D.: PlanetP: Using Gossiping to Build Content Addressable Peer-to-Peer Information Sharing Communities. In: Twelfth IEEE International Symposium on High Performance Distributed Computing (HPDC-12), pp. 236–246. IEEE Press, Los Alamitos (2003)Google Scholar
  8. 8.
    Demers, A., Greene, D., Hauser, C., Irish, W., Larson, J., Shenker, S., Sturgis, H., Swinehart, D., Terry, D.: Epidemic algorithms for replicated database maintenance. In: Proceedings of 6th ACM Symposium on Principles of Distributed Computing, Vancouver, British Columbia, Canada, pp. 1–12 (1987)Google Scholar
  9. 9.
    Jung, H., Tambe, M.: Argumentation as distributed constraint satisfaction: Applications and results. In: Proceedings of the fifth international conference on Autonomous agents (AGENTS 2001), pp. 324–331 (2001)Google Scholar
  10. 10.
    Kapferer, J.-N.: Rumeurs, le plus vieux média du monde. Points Actuel (1990)Google Scholar
  11. 11.
    Karunatillake, N.C., Jennings, N.R.: Is it worth arguing? In: Rahwan, I., Moraïtis, P., Reed, C. (eds.) ArgMAS 2004. LNCS, vol. 3366, pp. 62–67. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Lin, M.-J., Marzullo, K.: Directional gossip: Gossip in a wide area network. In: Hlavicka, J., Maehle, E., Pataricza, A. (eds.) EDDC 1999. LNCS, vol. 1667, p. 364. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  13. 13.
    Parsons, S., Sierra, C., Jennings, N.R.: Agents that reason and negotiate by arguing. Journal of Logic and Computation 8(3), 261–292 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Poole, D.: Explanation and prediction: An architecture for default and abductive reasoning. Computational Intelligence 5(2), 97–110 (1989)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Poole, D.: A methodology for using a default and abductive reasoning system. International Journal of Intelligent Systems 5, 521–548 (1990)zbMATHCrossRefGoogle Scholar
  16. 16.
    Roos, N., ten Tije, A., Witteveen, C.: A protocol for multi-agent diagnosis with spatially distributed knowledge. In: Proceedings of the Second international joint conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2003), pp. 655–661 (2003)Google Scholar
  17. 17.
    Roos, N., ten Tije, A., Witteveen, C.: Reaching diagnostic agreement in multiagent diagnosis. In: Proceedings of the Third International joint conference on Autonomous Agents and Multi-Agent System (AAMAS 2004), pp. 1254–1255 (2004)Google Scholar
  18. 18.
    Saks, M., Shavit, N., Woll, H.: Optimal time randomized consensus - making resilient algorithms fast in practice. In: Proceedings of the 2nd ACM-SIAM Symposium on Discrete Algorithms, pp. 351–362 (1991)Google Scholar
  19. 19.
    Even, S., Monien, B.: On the number of rounds needed to disseminate information. In: Proceedings of the First Annual ACM Symposium on Parallel Algorithms and Architectures, pp. 318–327 (1989)Google Scholar
  20. 20.
    Shibutani, T.: Improvised News: A Sociological Study of Rumor, Indianapolis and New york (1966)Google Scholar
  21. 21.
    Ye, F., Zhong, G., Lu, S., Zhang, L.: Gradient broadcast: A robust data delivery protocol for large scale sensor networks. ACM Wireless Networks 11(2) (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gauvain Bourgne
    • 1
  • Nicolas Maudet
    • 1
  • Suzanne Pinson
    • 1
  1. 1.LAMSADE, Université Paris-DauphineParis Cedex 16France

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