Interactive Learning of Expert Criteria for Rescue Simulations

  • Thanh-Quang Chu
  • Alain Boucher
  • Alexis Drogoul
  • Duc-An Vo
  • Hong-Phuong Nguyen
  • Jean-Daniel Zucker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5357)

Abstract

The goal of our work is to build a DSS (Decision Support System) to support resource allocation and planning for natural disaster emergencies in urban areas such as Hanoi in Vietnam. The first step has been to conceive a multi-agent environment that supports simulation of disasters, taking into account geospatial, temporal and rescue organizational information. The problem we address is the acquisition of situated expert knowledge that is used to organize rescue missions. We propose an approach based on participatory techniques, interactive learning and machine learning. This paper presents an algorithm that incrementally builds a model of the expert knowledge by online analysis of its interaction with the simulator’s proposed scenario.

Keywords

Rescue Management Multi-agent Simulation Decision Support Systems Knowledge Extraction Participatory Learning Interactive Learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Özdamar, L., Yi, W.: Greedy Neighborhood Search for Disaster Relief and Evacuation Logistics. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  2. 2.
    Paquet, S., Bernier, N., Chaib-draa, B.: An Online POMDP Algorithm for Complex Multiagent Environments. In: AAMAS 2005, Utrecht, Netherlands (July 2005)Google Scholar
  3. 3.
    Takahashi, T.: Agent-Based Disaster Simulation Evaluation and its Probability Model Interpretation. In: ISCRAM 2007 (2007)Google Scholar
  4. 4.
    Suárez, S., López, B., de La Rosa, J.L.: Co-operation strategies for strengthening civil agents’ lives in the RoboCup-Rescue simulator scenario. In: First International Workshop on Synthetic Simulation and Robotics to Mitigate Earthquake Disaster, Workshop Padova (2003)Google Scholar
  5. 5.
    Farinelli, A., Grisetti, G., Iocchi, L., Lo Cascio, S., Nardi, D.: Using the RoboCup-Rescue Simulator in an Italian Earthquake Scenario. In: The program Agenzia 2000 of the Italian Consiglio Nazionale delle RicercheGoogle Scholar
  6. 6.
    Paquet, S., Bernier, N., Chaib-draa, B.: Comparison of Different Coordination Strategies for the RoboCupRescue Simulation. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS, vol. 3029. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Drogoul, A., Ferber, J.: Multi-Agent Simulation as a Tool for Modeling Societies: Application to Social Differentiation in Ant Colonies. In: Castelfranchi, C., Werner, E. (eds.) MAAMAW 1992. LNCS, vol. 830, pp. 2–23. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  8. 8.
    Nguyen-Hong, P.: Decision support systems applied to earthquake and tsunami risk assessment and loss mitigation. In: Proceedings of IHOCE 2005, Kuala Lumpur, Malaysia (2005)Google Scholar
  9. 9.
    Sempé, F., Nguyen-Duc, M., Boucher, A., Drogoul, A.: An artificial maieutic approach for eliciting experts’ knowledge in multi-agent simulations. In: Sichman, J.S., Antunes, L. (eds.) MABS 2005. LNCS, vol. 3891. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Ramesh, R., Eisenberg, J., Schmitt, T.: Improving Disaster Management: The Role of IT in Mitigation, Preparedness, Response, and Recovery. Committee on Using Information Technology to Enhance Disaster Management, National Research Council, Washington, USA (2005)Google Scholar
  11. 11.
    Nguyen-Duc, M.: Vers la conception participative de simulations sociales: Application à la gestion du trafic aérien. Thèse de doctorat de l’université de Paris 6 (2007)Google Scholar
  12. 12.
    McCallum, A.K.: Reinforcement Learning with Selective Perception and Hidden State. PhD thesis, University of Rochester, Rochester, New-York (1996)Google Scholar
  13. 13.
    Paquet, S.: Distributed Decision-Making and Task Coordination in Dynamic, Uncertain and Real-Time Multiagent Environments. PhD thesis, Faculté de Sciences et Génie, Université Laval, Québec (2006)Google Scholar
  14. 14.
    Amouroux, E., Chu, T., Boucher, A., Drogoul, A.: GAMA: an environment for implementing and running spatially explicit multi-agent simulations. In: 10th Pacific Rim International Workshop on Multi-Agents (PRIMA), Bangkok, Thailand (2007)Google Scholar
  15. 15.
    Zinflou, A.: Système interactif d’aide à la décision basé sur des algorithmes génétiques pour l’optimisation multi-objectifs, Master thesis, Université du Québec à Chicoutimi, pp. 46–50 (2004)Google Scholar
  16. 16.
    Nguyen-Duc, M., Drogoul, A.: Using Computational Agents to Design Participatory Social Simulations. Journal of Artificial Societies and Social Simulation 10(45) (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thanh-Quang Chu
    • 1
    • 2
  • Alain Boucher
    • 2
  • Alexis Drogoul
    • 1
    • 2
  • Duc-An Vo
    • 1
    • 2
  • Hong-Phuong Nguyen
    • 3
  • Jean-Daniel Zucker
    • 1
  1. 1.IRD, UR079-GEODESBondy CedexFrance
  2. 2.AUF-IFI, MSIHa NoiViet Nam
  3. 3.IG-VASTHanoiVietnam

Personalised recommendations