Multilevel MAS Architecture for Vehicles Knowledge Propagating

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 156)


Completely autonomous vehicles in traffic should allow to decrease the number of road accident victims greatly, and should allow gains in terms of performance and economy. Models of the interaction among the different vehicles is one of the main challenges. We propose in this paper a model of communication of knowledge between mobile agents on a traffic network. The model of knowledge and of interaction enables to propagate new knowledge without overloading the system with a too large number of communications. For that, only the new knowledge is communicated, and two agents communicate the same knowledge only once. In order to allow agents to update their knowledge (perceived or created), a notion of degradation is used. A simulator has been built to evaluate the proposal.


Multiagent System Mobile Agent Good Path Autonomous Vehicle Road Sign 
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.
    Adam, E., Zambrano, G., Pach, C., Berger, T., Trentesaux, D.: Myopic Behaviour in Holonic Multiagent Systems for Distributed Control of FMS. In: Corchado, J.M., Pérez, J.B., Hallenborg, K., Golinska, P., Corchuelo, R. (eds.) Trends in PAAMS. AISC, vol. 90, pp. 91–98. Springer, Heidelberg (2011)Google Scholar
  2. 2.
    Bazzan, A.L.: A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multi-Agent Systems 10, 131–164 (2005)CrossRefGoogle Scholar
  3. 3.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Dresner, K., Stone, P.: Mitigating catastrophic failure at intersections of autonomous vehicles. In: AAMAS Workshop on Agents in Traffic and Transportation, Estoril, Portugal, pp. 78–85 (2008)Google Scholar
  5. 5.
    Ferber, J.: Multi-agent systems - an introduction to distributed artificial intelligence. Addison-Wesley-Longman (1999)Google Scholar
  6. 6.
    Jennings, N.R., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Int. Journal of Autonomous Agents and Multi-Agent Systems 1(1), 7–38 (1998)CrossRefGoogle Scholar
  7. 7.
    Mandiau, R., Champion, A., Auberlet, J.-M., Espié, S., Kolski, C.: Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation. Appl. Intell. 28(2), 121–138 (2008)CrossRefGoogle Scholar
  8. 8.
    Popovici, D., Desertot, M., Lecomte, S., Peon, N.: Context-aware transportation services (cats) framework for mobile environments. IJNGC 2(1) (2011)Google Scholar
  9. 9.
    Ruskin, H.J., Wang, R.: Modelling Traffic Flow at an Urban Unsignalised Intersection. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J., Hoekstra, A.G. (eds.) ICCS-ComputSci 2002, Part I. LNCS, vol. 2329, pp. 381–390. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Vercouter, L., Jamont, J.-P.: Lightweight trusted routing for wireless sensor networks. In: Demazeau, Y., Pechoucek, M., Corchado, J.M., Pérez, J.B. (eds.) PAAMS. AISC, vol. 88, pp. 87–96. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Emmanuel Adam
    • 1
    • 2
    • 3
  • René Mandiau
    • 1
    • 2
    • 3
  • Emmanuelle Grislin
    • 1
    • 2
    • 3
  1. 1.Lille Nord de FranceLilleFrance
  2. 2.UVHC, LAMIHValenciennesFrance
  3. 3.CNRS, UMR 8201ValenciennesFrance

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