Advertisement

A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

  • Jean-Baptiste Soyez
  • Gildas Morvan
  • Daniel Dupont
  • Rochdi Merzouki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7838)

Abstract

This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.

Keywords

agent-based simulation multi-scales IRM4MLS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Caillou, P., Gil-Quijano, J.: Simanalyzer: Automated description of groups dynamics in agent-based simulations. In: Proc. of 11th Int. Conf. on Autonomous Agents and Multiagent Systems, AAMAS 2012 (2012)Google Scholar
  2. 2.
    Caillou, P., Gil-Quijano, J., Zhou, X.: Automated observation of multi-agent based simulations: a statistical analysis approach. To appear in Studia Informatica Universalis (2013)Google Scholar
  3. 3.
    Chen, C., Clack, C., Nagl, S.: Identifying multi-level emergent behaviors in agent-directed simulations using complex event type specifications. Simulation 86(1), 41–51 (2010)CrossRefGoogle Scholar
  4. 4.
    Chen, C., Nagl, S., Clack, C.: A formalism for multi-level emergent behaviours in designed component-based systems and agent-based simulations. In: Aziz-Alaoui, M., Bertelle, C. (eds.) From System Complexity to Emergent Properties, Understanding Complex Systems, vol. 12, pp. 101–114. Springer (2009)Google Scholar
  5. 5.
    David, D., Courdier, R.: See emergence as a metaknowledge. a way to reify emergent phenomena in multiagent simulations? In: Proceedings of ICAART 2009, Porto, Portugal, pp. 564–569 (2009)Google Scholar
  6. 6.
    Davis, P., Hillestad, R.: Families of model that cross levels of resolution: Issues for design, calibration and management. In: 25th Winter Simulation Conference, WSC 1993 (1993)Google Scholar
  7. 7.
    Ferber, J., Müller, J.P.: Influences and reaction: a model of situated multiagent systems. In: 2nd International Conference on Multi-Agent Systems (ICMAS 1996), pp. 72–79 (1996)Google Scholar
  8. 8.
    Gaud, N., Galland, S., Gechter, F., Hilaire, V., Koukam, A.: Holonic multilevel simulation of complex systems: Application to real-time pedestrians simulation in virtual urban environment. Simulation Modelling Practice and Theory 16, 1659–1676 (2008)CrossRefGoogle Scholar
  9. 9.
    Gil-Quijano, J., Louail, T., Hutzler, G.: From biological to urban cells: Lessons from three multilevel agent-based models. In: Desai, N., Liu, A., Winikoff, M. (eds.) PRIMA 2010. LNCS, vol. 7057, pp. 620–635. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Michel, F.: The irm4s model: the influence/reaction principle for multiagent based simulation. In: AAMAS 2007: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1–3. ACM, New York (2007)CrossRefGoogle Scholar
  11. 11.
    Michel, F., Gouaïch, A., Ferber, J.: Weak interaction and strong interaction in agent based simulations. In: MABS 2003. LNCS, vol. 2927, pp. 43–56. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Moncion, T., Amar, P., Hutzler, G.: Automatic characterization of emergent phenomena in complex systems. Journal of Biological Physics and Chemistry 10, 16–23 (2010)CrossRefGoogle Scholar
  13. 13.
    Morvan, G.: Multi-level agent-based modeling - bibliography. CoRR abs/1205.0561 (May 2012)Google Scholar
  14. 14.
    Morvan, G., Jolly, D.: Multi-level agent-based modeling with the Influence Reaction principle. CoRR abs/1204.0634 (April 2012)Google Scholar
  15. 15.
    Morvan, G., Veremme, A., Dupont, D.: IRM4MLS: The influence reaction model for multi-level simulation. In: Bosse, T., Geller, A., Jonker, C.M. (eds.) MABS 2010. LNCS, vol. 6532, pp. 16–27. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Navarro, L., Flacher, F., Corruble, V.: Dynamic level of detail for large scale agent-based urban simulations. In: Tumer, Y., Sonenberg, S. (eds.) 10th Int. Conf on Autonomous Agents and Multiagent Systems (AAMAS 2011), pp. 701–708 (2011)Google Scholar
  17. 17.
    Picault, S., Mathieu, P.: An interaction-oriented model for multi-scale simulation. In: The 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011) (2011)Google Scholar
  18. 18.
    Scerri, D., Hickmott, S., Drogoul, A., Padgham, L.: An architecture for distributed simulation with agent-based models. In: van der Hoek, Kaminka, Lespérance, Luck, Sen (eds.) Proc. of 9th Int. Conf on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, May 10-14, pp. 541–548 (2010)Google Scholar
  19. 19.
    Simulation Interoperability Standards Comittee (SISC): IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) - Framework and Rules. IEEE Computer Society (2000)Google Scholar
  20. 20.
    Soyez, J.B., Morvan, G., Merzouki, R., Dupont, D., Kubiak, P.: Multi-agent multi-level modeling – a methodology to simulate complex systems. In: Proceedings of the 23rd European Modeling & Simulation Symposium (2011)Google Scholar
  21. 21.
    Stratulat, T., Ferber, J., Tranier, J.: Masq: toward an integral approach to interaction. In: Proceedings of the 8th Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009), pp. 813–820 (2009)Google Scholar
  22. 22.
    Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P.: Reference architecture for holonic manufacturing systems: Prosa. Computers in Industry 37(3), 255–274 (1998)CrossRefGoogle Scholar
  23. 23.
    Vo, D.-A., Drogoul, A., Zucker, J.-D., Ho, T.-V.: A modelling language to represent and specify emerging structures in agent-based model. In: Desai, N., Liu, A., Winikoff, M. (eds.) PRIMA 2010. LNCS (LNAI), vol. 7057, pp. 212–227. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Weyns, D., Holvoet, T.: Model for simultaneous actions in situated multi-agent systems. In: Schillo, M., Klusch, M., Müller, J., Tianfield, H. (eds.) MATES 2003. LNCS (LNAI), vol. 2831, pp. 105–118. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  25. 25.
    Zeigler, B., Kim, T., Praehofer, H.: Theory of Modeling and Simulation, 2nd edn. Academic Press (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jean-Baptiste Soyez
    • 1
    • 2
  • Gildas Morvan
    • 1
    • 3
  • Daniel Dupont
    • 1
    • 4
  • Rochdi Merzouki
    • 1
    • 2
  1. 1.Univ. Lille Nord de FranceLille cedexFrance
  2. 2.LAGIS UMR CNRS 8146 École Polytechnique de LilleVilleneuve d’AscqFrance
  3. 3.LGI2AUniv. Artois Technoparc FuturaBéthuneFrance
  4. 4.HEIUC LilleLille cedexFrance

Personalised recommendations