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

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Multi-Agent-Based Simulation XIII (MABS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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References

  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. 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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  13. Morvan, G.: Multi-level agent-based modeling - bibliography. CoRR abs/1205.0561 (May 2012)

    Google Scholar 

  14. Morvan, G., Jolly, D.: Multi-level agent-based modeling with the Influence Reaction principle. CoRR abs/1204.0634 (April 2012)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  25. Zeigler, B., Kim, T., Praehofer, H.: Theory of Modeling and Simulation, 2nd edn. Academic Press (2000)

    Google Scholar 

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Soyez, JB., Morvan, G., Dupont, D., Merzouki, R. (2013). A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations. In: Giardini, F., Amblard, F. (eds) Multi-Agent-Based Simulation XIII. MABS 2012. Lecture Notes in Computer Science(), vol 7838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38859-0_10

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  • DOI: https://doi.org/10.1007/978-3-642-38859-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38858-3

  • Online ISBN: 978-3-642-38859-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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