Enhanced Maritime Situation Awareness with Negotiator Agents

  • Miniar Hemaissia
  • Amal El Fallah Seghrouchni
  • Juliette Mattioli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3890)


French coastguard missions have become increasingly varied implying new challenges such as the reduction of the decision cycle and the expansion of available information. Thus, it involves new needs for enhanced decision support. An efficient situation awareness system has to quickly detect and identify suspicious boats. The efficiency of such a system relies on a reliable sensor fusion since a coastguard uses sensors to achieve his mission. We present an innovative approach based on multi-agent negotiation to fuse classifiers, benefiting from the efficiency of existing classification tools and from the flexibility and reliability of a multi-agent system to exploit distributed data across dispersed sources. We developed a first prototype using a basic negotiation protocol in order to validate the feasibility and the interest of our approach. The results obtained are promising and encourage us to continue on this way.


Multiagent System Mobile Agent Situation Awareness Information Fusion Interaction Initiator 
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.
    Ruta, D., Gabrys, B.: An overview of classifier fusion methods. Computing and Information Systems 7, 1–10 (2000)Google Scholar
  2. 2.
    Ferber, J., Gutknecht, O.: A meta-model for the analysis and design of organizations in multi-agent systems. In: Third International Conference on Multi-Agent Systems, ICMAS 1998, pp. 128–135 (1998)Google Scholar
  3. 3.
    El Fallah-Seghrouchni, A., Suna, A.: CLAIM: A computational language for autonomous, intelligent and mobile agents. In: Dastani, M.M., Dix, J., El Fallah-Seghrouchni, A. (eds.) PROMAS 2003. LNCS (LNAI), vol. 3067, pp. 90–110. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    El Fallah-Seghrouchni, A., Suna, A.: Himalaya framework: Hierarchical intelligent mobile agents for building large-scale and adaptive systems based on ambients. In: Ishida, T., Gasser, L., Nakashima, H. (eds.) MMAS 2005. LNCS (LNAI), vol. 3446, pp. 202–216. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Gorodetsky, V., Karsaev, O., Kotenko, I., Samoilov, V.: Multi-agent information fusion: methodology, architecture and software tool for learning of object and situation assessment. In: Seventh International Conference on Information Fusion (Fusion 2004), Stockholm, Sweden, pp. 346–353 (2004)Google Scholar
  6. 6.
    Chen, T.M., Luo, R.C.: Multilevel multiagent based team decision fusion for autonomous tracking system. International Journal on Machine Intelligence and Robotic Control 1, 489–494 (2000)Google Scholar
  7. 7.
    Oxenham, M.G., Challa, S., Morelande, M.R.: Decentralised fusion of disparate identity estimates for shared situation awareness. In: Seventh International Conference on Information Fusion (Fusion 2004), Stockholm, Sweden, pp. 167–174 (2004)Google Scholar
  8. 8.
    SMA2 Consortium: Rapport de synthèse finale. A french project, SMA2 Consortium (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miniar Hemaissia
    • 1
  • Amal El Fallah Seghrouchni
    • 2
  • Juliette Mattioli
    • 1
  1. 1.PLATON LabTHALES Research & Technology FrancePalaiseauFrance
  2. 2.UMR 7606 – CNRSUniversité Paris 6, Laboratoire d’Informatique de Paris 6ParisFrance

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