Advertisement

Building Optimal Macroscopic Representations of Complex Multi-agent Systems

Application to the Spatial and Temporal Analysis of International Relations Through News Aggregation
  • Robin Lamarche-PerrinEmail author
  • Yves Demazeau
  • Jean-Marc Vincent
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8670)

Abstract

The design and the debugging of large-scale MAS require abstraction tools in order to work at a macroscopic level of description. Agent aggregation provides such abstractions by reducing the complexity of the system’s microscopic representation. Since it leads to an information loss, such a key process may be extremely harmful for the analysis if poorly executed. This paper presents measures inherited from information theory to evaluate abstractions and to provide the experts with feedback regarding the quality of generated representations. Several evaluation techniques are applied to the spatial and temporal aggregation of an agent-based model of international relations. The information from on-line newspapers constitutes a complex microscopic representation of the agent states. Our approach is able to evaluate geographical abstractions used by the domain experts in order to provide efficient and meaningful macroscopic representations of the world global state.

Keywords

Large-scale MAS Agent aggregation Macroscopic representation Information theory Geographical and news analysis 

Notes

Acknowledgement

This work was partially funded by the ANR CORPUS GEOMEDIA project (ANR-GUI-AAP-04). We would like to thank Claude Grasland, Timothée Giraud and Marta Severo for their work on this project; and Lucas M. Schnorr for his close participation to previous work.

References

  1. 1.
    Elmqvist, N., Fekete, J.: Hierarchical aggregation for information visualization: overview, techniques, and design guidelines. IEEE Trans. Visual Comput. Graphics 16(3), 439–454 (2010)CrossRefGoogle Scholar
  2. 2.
    Kullback, S., Leibler, R.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27(379–423), 623–656 (1948)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Railsback, S.F., Lytinen, S.L., Jackson, S.K.: Agent-based simulation platforms: review and development recommendations. Simulation 82, 609–623 (2006)CrossRefGoogle Scholar
  5. 5.
    Van Liedekerke, M.H., Avouris, N.M.: Debugging multi-agent systems. Inf. Softw. Technol. 37, 103–112 (1995)CrossRefGoogle Scholar
  6. 6.
    Búrdalo, L., Terrasa, A., Julián, V., García-Fornes, A.: A tracing system architecture for self-adaptive multiagent systems. In: Demazeau, Y., Dignum, F., Corchado, J.M., Pérez, J.B. (eds.) Advances in PAAMS. AISC, vol. 70, pp. 205–210. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  7. 7.
    Tonn, J., Kaiser, S.: ASGARD – a graphical monitoring tool for distributed agent infrastructures. In: Demazeau, Y., Dignum, F., Corchado, J.M., Pérez, J.B. (eds.) Advances in PAAMS. AISC, vol. 70, pp. 163–173. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  8. 8.
    Sharpanskykh, A., Treur, J.: Group abstraction for large-scale agent-based social diffusion models with unaffected agents. In: Kinny, D., Hsu, J.Y., Governatori, G., Ghose, A.K. (eds.) PRIMA 2011. LNCS, vol. 7047, pp. 129–142. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  9. 9.
    Iravani, P.: Multi-level network analysis of multi-agent systems. In: Iocchi, L., Matsubara, H., Weitzenfeld, A., Zhou, C. (eds.) RoboCup 2008. LNCS (LNAI), vol. 5399, pp. 495–506. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  10. 10.
    Peng, W., Grushin, A., Manikonda, V., Krueger, W., Carlos, P., Santos, M.: Graph-based methods for the analysis of large-scale multiagent systems. In: AAMAS’09, IFAAMAS, pp. 545–552 (2009)Google Scholar
  11. 11.
    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
  12. 12.
    Grasland, C., Didelon, C.: Europe in the World - Final Report. Volume 1, ESPON Project 3.4.1 (2007)Google Scholar
  13. 13.
    United Nations Environment Programme: Global Environmental Outlook: environment for development. Volume 4, Nairobi (2007)Google Scholar
  14. 14.
    Csiszár, I.: Axiomatic characterizations of information measures. Entropy 10(3), 261–273 (2008)CrossRefzbMATHGoogle Scholar
  15. 15.
    Galtung, J., Ruge, M.H.: The structure of foreign news: the presentation of the Congo, Cuba and Cyprus crises in four Norwegian newspapers. J. Peace Res. 2(1), 64–91 (1965)CrossRefGoogle Scholar
  16. 16.
    Koopmans, R., Vliegenthart, R.: Media attention as the outcome of a diffusion process–a theoretical framework and cross-national evidence on earthquake coverage. Eur. Sociol. Rev. 27(5), 636–653 (2011)CrossRefGoogle Scholar
  17. 17.
    Deguet, J., Demazeau, Y., Magnin, L.: Element about the emergence issue: a survey of emergence definitions. ComPlexUs 3, 24–31 (2006)CrossRefGoogle Scholar
  18. 18.
    Lamarche-Perrin, R., Vincent, J.M., Demazeau, Y.: Informational measures of aggregation for complex systems analysis. Technical report RR-LIG-026, Laboratoire d’Informatique de Grenoble, France (2012)Google Scholar
  19. 19.
    Lamarche-Perrin, R., Demazeau, Y., Vincent, J.M.: The best-partitions problem: how to build meaningful aggregations. In: Proceedings of the 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’13), Atlanta, GA, USA, pp. 399–404. IEEE Computer Society (2013)Google Scholar
  20. 20.
    Jackson, B., Scargle, J.D., Barnes, D., Arabhi, S., Alt, A., Gioumousis, P., Gwin, E., Sangtrakulcharoen, P., et al.: An algorithm for optimal partitioning of data on an interval. IEEE Signal Process. Lett. 12(2), 105–108 (2005)CrossRefGoogle Scholar
  21. 21.
    Lamarche-Perrin, R., Schnorr, L.M., Vincent, J.M., Demazeau, Y.: Evaluating trace aggregation for performance visualization of large distributed systems. In: Proceedings of the 2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS’14), Monterey, CA, USA (2014)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Robin Lamarche-Perrin
    • 1
    Email author
  • Yves Demazeau
    • 2
  • Jean-Marc Vincent
    • 1
  1. 1.LIGUniversité Grenoble AlpesGrenobleFrance
  2. 2.LIGCNRSGrenobleFrance

Personalised recommendations