GAMA: A Simulation Platform That Integrates Geographical Information Data, Agent-Based Modeling and Multi-scale Control

  • Patrick Taillandier
  • Duc-An Vo
  • Edouard Amouroux
  • Alexis Drogoul
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7057)

Abstract

The agent-based modeling is now widely used to study complex systems. Its ability to represent several levels of interaction along a detailed (complex) environment representation favored such a development. However, in many models, these capabilities are not fully used. Indeed, only simple, usually discrete, environment representation and one level of interaction (rarely two or three) are considered in most of the agent-based models. The major reason behind this fact is the lack of simulation platforms assisting the work of modelers in these domains. To tackle this problem, we developed a new simulation platform, GAMA. This platform allows modelers to define spatially explicit and multi-levels models. In particular, it integrates powerful tools coming from Geographic Information Systems (GIS) and Data Mining easing the modeling and analysis efforts. In this paper, we present how this platform addresses these issues and how such tools are available right out of the box to modelers.

Keywords

Simulation platform Agent-based modeling Geographical vector data Multi-level control 

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References

  1. 1.
    Amouroux, E., Chu, T.-Q., Boucher, A., Drogoul, A.: GAMA: An Environment for Implementing and Running Spatially Explicit Multi-agent Simulations. In: Ghose, A., Governatori, G., Sadananda, R. (eds.) PRIMA 2007. LNCS, vol. 5044, pp. 359–371. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Amouroux, E., Desvaux, S., Drogoul, A.: Towards Virtual Epidemiology: An Agent-Based Approach to the Modeling of H5N1 Propagation and Persistence in North-Vietnam. In: Bui, T.D., Ho, T.V., Ha, Q.T. (eds.) PRIMA 2008. LNCS (LNAI), vol. 5357, pp. 26–33. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Nguyen Vu, Q.A., Gaudou, B., Canal, R., Hassas, S.: Coherence and robustness in a disturbed MAS. In: IEEE-RIVF, Danang, Vietnam. IEEE (2009)Google Scholar
  5. 5.
    Chu, T.Q., Drogoul, A., Boucher, A., Zucker, J.: Interactive Learning of Independent Experts’ Criteria for Rescue Simulations. Journal of Universal Computer Science 15(13), 2701–2725 (2009)Google Scholar
  6. 6.
    Taillandier, P., Buard, E.: Designing Agent Behaviour in Agent-Based Simulation Through Participatory Method. In: Yang, J.-J., Yokoo, M., Ito, T., Jin, Z., Scerri, P. (eds.) PRIMA 2009. LNCS, vol. 5925, pp. 571–578. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Ruas, A., Duchêne, C.: A prototype generalisation system based on the multi-agent system paradigm. In: Generalisation of Geographic Information: Cartographic Modelling and Applications, pp. 269–284. Elsevier Ltd. (2007)Google Scholar
  8. 8.
    Minar, N., Burkhart, R., Langton, C., Askenazi, M.: The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations, SFI Working Paper 96-06-042 (1996)Google Scholar
  9. 9.
    Box, P.: Spatial Units as Agents. In: Integrating GIS and Agent-Based Modelling Techniques, Oxford (2002)Google Scholar
  10. 10.
    Haklay, M., O’Sullivan, D., Thurstain-Goodwin, M., Schelhorn, T.: So Go Downtown: Simulating Pedestrian Movement in Town Centres. Environment and Planning B: Planning and Design 28(3), 343–359 (2001)CrossRefGoogle Scholar
  11. 11.
    Wilensky, U.: NetLogo. In: Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston (1999), http://ccl.northwestern.edu/netlogo/
  12. 12.
    Russell, E., Wilensky, U.: Consuming spatial data in NetLogo using the GIS Extension. In: The Annual Meeting of the Swarm Development Group, Chicago, IL (2008)Google Scholar
  13. 13.
    Bousquet, F., Bakam, I., Proton, H., Le Page, C.: Cormas: common-pool resources and multi-agents systems. In: IEA/AIE, vol. 2, pp. 826–837 (1998)Google Scholar
  14. 14.
    Urbani, D., Delhom, M.: Analyzing Knowledge Exchanges in Hybrid MAS GIS Decision Support Systems, Toward a New DSS Architecture. In: Nguyen, N.T., Jo, G.-S., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2008. LNCS (LNAI), vol. 4953, pp. 323–332. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    North, M.J., Collier, N.T., Vos, J.R.: Experiences Creating Three Implementations of the Repast Agent Modeling Toolkit. ACM Transactions on Modeling and Computer Simulation 16(1), 1–25 (2006)CrossRefGoogle Scholar
  16. 16.
    North, M.J., Tatara, E., Collier, N.T., Ozik, J.: Visual Agent-based Model Development with Repast Simphony. In: Conference on Complex Interaction and Social Emergence (2007)Google Scholar
  17. 17.
    Dijkstra, E.W.: A short introduction to the art of programming. Technological Univ. Eindhoven, Rep. EWD316 (1971)Google Scholar
  18. 18.
    Floyd, R.W.: Algorithm 97: Shortest Path. Communications of the ACM 5(6), 345 (1962)CrossRefGoogle Scholar
  19. 19.
    Camazine, S., et al.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2001)Google Scholar
  20. 20.
  21. 21.
    Breton, L., Zucker, J.-D., Clément, E.: A Multi-Agent Based Simulation of Sand Piles in a Static Equilibrium. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 108–118. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  22. 22.
    Pelleg, D., Moore, A.W.: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: International Conference on Machine Learning, pp. 727–734 (2000)Google Scholar
  23. 23.
    Gennari, J.H., Langley, P., Fisher, D.: Models of incremental concept formation. Artificial Intelligence 40, 11–61 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Patrick Taillandier
    • 1
    • 2
  • Duc-An Vo
    • 1
    • 2
  • Edouard Amouroux
    • 1
    • 2
  • Alexis Drogoul
    • 1
    • 2
  1. 1.IRD, UMI UMMISCO 209BondyFrance
  2. 2.IFI, MSI, UMI 209HanoiVietnam

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