CFBM - A Framework for Data Driven Approach in Agent-Based Modeling and Simulation

  • Thai Minh Truong
  • Frédéric Amblard
  • Benoit Gaudou
  • Christophe Sibertin Blanc
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 168)

Abstract

Recently, there has been a shift from modeling driven approach to data driven approach in Agent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models [1, 2]. In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, evaluation of the output of the simulation platform. That raises the question how to manage empirical data, simulation data and compare those data in such agent-based simulation platform.

In this paper, we first introduce a logical framework for data driven approach in agent-based modeling and simulation. The introduced framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform). Secondly, we demonstrate the application of CFBM for data driven approach via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to initialize and validate the models.

The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach.

Keywords

Agent-Based model BI solution Brown plant hopper Data driven approach Data warehouse Multi-Agent based simulation 

References

  1. 1.
    Edmonds, B., Moss, S.: From KISS to KIDS – an ‘anti-simplistic’ modelling approach. In: Davidsson, P., Logan, B., Takadama, K. (eds.) MABS 2004. LNCS (LNAI), vol. 3415, pp. 130–144. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Hassan, S.: Towards a Data-driven Approach for Agent-Based Modelling: Simulating Spanish (2009)Google Scholar
  3. 3.
    Becu, N., Perez, P., Walker, A., Barreteau, O., Page, C.L.: Agent based simulation of a small catchment water management in Northern Thailand. Ecol. Modell. 170, 319–331 (2003)CrossRefGoogle Scholar
  4. 4.
    Gaudou, B., Sibertin-Blanc, C., Therond, O., Amblard, F., Arcangeli, J.-P., Balestrat, M., Charron-Moirez, M.-H., Gondet, E., Hong, Y., Louail, T., Mayor, E., Panzoli, D., Sauvage, S., Sanchez-Perez, J.-M., Taillandier, P., Nguyen, V.B., Vavasseur, M., Mazzega, P.: The maelia multi-agent platform for integrated assessment of low-water management issues. In: Multi-Agent-Based Simulation XIV-International Workshop, MABS (2013)Google Scholar
  5. 5.
    Rao, D.M., Chernyakhovsky, A., Rao, V.: Modeling and analysis of global epidemiology of avian influenza. Environ. Model Softw. 24, 124–134 (2009)CrossRefGoogle Scholar
  6. 6.
    Stroud, P., Valle, S. Del, Sydoriak, S., Riese, J., Mniszewski, S.: Spatial dynamics of pandemic influenza in a massive artificial society. Artif. Soc. Soc. Simul. 10 (2007)Google Scholar
  7. 7.
    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
  8. 8.
    Dunham, J.: An agent-based spatially explicit epidemiological model in MASON. J. Artif. Soc. Soc. Simul. 9 (2005)Google Scholar
  9. 9.
    Sibertin-Blanc, C., Roggero, P., Adreit, F., Baldet, B., Chapron, P., El-Gemayel, J., Mailliard, M., Sandri, S.: SocLab: A Framework for the modeling, simulation and analysis of power in social organizations. J. Artif. Soc. Soc. Simul. 16 (2013)Google Scholar
  10. 10.
    Barnaud, C., Bousquet, F., Trebuil, G.: Multi-agent simulations to explore rules for rural credit in a highland farming community of Northern Thailand. Ecol. Econ. 66, 615–627 (2008)CrossRefGoogle Scholar
  11. 11.
    Hassan, S., Antunes, L., Pavón, J.: Mentat: a data-driven agent-based simulation of social values evolution. In: Di Tosto, G., Van Dyke Parunak, H. (eds.) MABS 2009. LNCS, vol. 5683, pp. 135–146. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Hassan, S., Pavon, J., Gilbert, N.: Injecting data into simulation: can agent-based modelling learn from microsimulation? In: The World Congress of Social Simulation 2008, Washington, D.C. (2008)Google Scholar
  13. 13.
    Hassan, S., Pavón, J., Antunes, L., Gilbert, N.: Injecting data into agent-based simulation. In: Takadama, K., Cioffi-Revilla, C., Deffuant, G. (eds.) Simulating Interacting Agents and Social Phenomena. Agent-Based Social Systems, vol. 7, pp. 177–191. Springer, Japan (2010)Google Scholar
  14. 14.
    Gilbert, N., Troitzsch, K.G.: Simulation for the Social Scientist. Open University Press, Buckingham (2005)Google Scholar
  15. 15.
    Mahboubi, H., Faure, T., Bimonte, S., Deffuant, G., Chanet, J.-P., Pinet, F.: A multidimensional model for data warehouses of simulation results. Int. J. Agric. Environ. Inf. Syst. 1, 1–19 (2010)CrossRefGoogle Scholar
  16. 16.
    Vasilakis, C., El-Darzi, E.: A data warehouse environment for storing and analyzing simulation output data. In: Simulation Conference (2004)Google Scholar
  17. 17.
    Inmon, W.H.: Building the Data Warehouse. Wiley Publishing Inc. (2005)Google Scholar
  18. 18.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. John Wiley & Sons Inc., New York (2002)Google Scholar
  19. 19.
    Madeira, H., Costa, J.P., Vieira, M.: The OLAP and data warehousing approaches for analysis and sharing of results from dependability evaluation experiments. In: International Conference on Dependable Systems and Networks, pp. 86–99 (2003)Google Scholar
  20. 20.
    Sosnowski, J., Zygulski, P., Gawkowski, P.: Developing data warehouse for simulation experiments. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 543–552. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Ehmke, J.F., Grosshans, D., Mattfeld, D.C., Smith, L.D.: Interactive analysis of discrete-event logistics systems with support of a data warehouse. Comput. Ind. 62, 578–586 (2011)CrossRefGoogle Scholar
  22. 22.
    Vasilakis, C., El-Darzi, E., Chountas, P.: A decision support system for measuring and modelling the multi-phase nature of patient flow in hospitals. In: Chountas, P., Petrounias, I., Kacprzyk, J. (eds.) Intelligent Techniques and Tools for Novel System Architectures. SCI, vol. 109, pp. 201–217. Springer, Heidelberg (2008)Google Scholar
  23. 23.
    Mahboubi, H., Faure, T., Bimonte, S., Deffuant, G., Chanet, J.P., Pinet, F.: A multidimensional model for data warehouses of simulation results. Int. J. Agric. Environ. Inf. Syst. 1, 1–19 (2010)CrossRefGoogle Scholar
  24. 24.
    Hassan, S., Antunes, L., Pavon, J., Gilbert, N.: Stepping on earth: a roadmap for data-driven agent-based modelling. In: The 5th Conference of the European Social Simulation Association (ESSA 2008), pp. 1–12 (2008)Google Scholar
  25. 25.
    Truong, T.M., Truong, V.X., Amblard, F., Sibertin-blanc, C., Drogoul, A., Le, M.N.: An Implementation of framework of business intelligence for agent-based simulation. In: The 4th International Symposium on Information and Communication Technology (SoICT 2013), pp. 35–44. ACM (2013)Google Scholar
  26. 26.
    Kimball, R., Ross, M.: Data Warehouse Toolkit: The Complete Guide to Dimentional Modeling. John Wiley & Sons Inc., New York (2002)Google Scholar
  27. 27.
    Ngo, T.D.: Simulating the flooding progression by the influence of rainfall in Can Tho city (2013)Google Scholar
  28. 28.
    Truong, V.X.: Optimization by simulation of an environmental surveillance network - application to the fight against rice pests in the mekong delta (vietnam) (2014)Google Scholar
  29. 29.
    Truong, T.M., Amblard, F., Benoit, G., Sibertin-blanc, C.: To calibrate & validate an agent-based simulation model an application of the combination framework of bi solution & multi-agent platform. In: The 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), pp. 172–183 (2014)Google Scholar
  30. 30.
    Phan, C.H., Huynh, H.X., Drogoul, A.: An agent-based approach to the simulation of Brown Plant Hopper (BPH) invasions in the Mekong Delta. In: 2010 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), pp. 1–6. IEEE (2010)Google Scholar
  31. 31.
    Truong, V.X., Drogoul, A., Huynh, H.X., Le, M.N.: Modeling the brown plant hoppers surveillance network using agent-based model - application for the Mekong Delta region. In: Proceedings of the Second Symposium on Information and Communication Technology, pp. 127–136. ACM (2011)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Thai Minh Truong
    • 1
    • 2
  • Frédéric Amblard
    • 2
  • Benoit Gaudou
    • 2
  • Christophe Sibertin Blanc
    • 2
  1. 1.CITCan Tho UniversityCan ThoVietnam
  2. 2.UMR 5505 CNRS-IRIT, Université Toulouse 1 CapitoleToulouseFrance

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