Advertisement

Analysis Methods of Agent Behavior and Its Interpretation in a Case of Rescue Simulations

  • Tomoichi Takahashi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5399)

Abstract

The agent-based approach has been proved to be useful for the modeling phenomena of traditional social fields. In order to apply agent-based simulation results to practical usages, it is necessary to show potential users the validity of the simulation outputs that arise out of the agents’ behaviors. In cases that involve human actions, it is difficult to obtain sufficient amounts of data on real cases or experimental data to validate the simulation results.

In this paper, we review the metrics that have been used to evaluate rescue agents in the Rescue Simulation Agent competition and propose a method to analyze the simulation output by presenting the agent behavior with a probability model. We present that the analysis results of the method are comparable to a human-readable interpretation and with task-dependent knowledge and discuss its applicability to real-world cases.

Keywords

Multi Agent System Agent Behavior Stochastic Matrix Rescue Agent Social Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Farkas, I., Helbing, D., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407, 487–490 (2000)CrossRefGoogle Scholar
  2. 2.
    Feynman, R.P.: The Character of Physical Law. MIT Press, Cambridge (1967)Google Scholar
  3. 3.
    Jennings, N.R.: Agent-based computing: Promise and perils. In: Proc. IJCAI 1999, pp. 1429–1436 (1999)Google Scholar
  4. 4.
    Johnson, C.W.: The application of computational models for the simulation of large scale evacuations following infrastructure failures and terrorist incidents. In: Proc. NATO Research Workshop on Computational Models of Risk to Infrastructure, pp. 9–13 (May 2006)Google Scholar
  5. 5.
    Kitano, H., Tadokoro, S., Noda, I., Matsubara, H., Takahashi, T., Shinjou, A., Shimada, S.: Robocup rescue: Search and rescue in large-scale disasters as a domain for autonomous agents research. In: IEEE International Conference on System, Man, and Cybernetics (1999)Google Scholar
  6. 6.
    Moss, S., Edmonds, B.: Towards good social science. Journal of Artificial Societies and Social Simulation 8(4) (2005)Google Scholar
  7. 7.
  8. 8.
    Schurr, N., Marecki, J., Kasinadhuni, N., Tambe, M., Lewis, J.P., Scerri, P.: The defacto system for human omnipresence to coordinate agent teams: The future of disaster response. In: AAMAS 2005, pp. 1229–1230 (2005)Google Scholar
  9. 9.
    Takahashi, T., Ito, N.: Preliminary study to use rescue simulation as check soft of urban’s disasters. In: Workshop: Safety and Security in MAS (SASEMAS) at AAMAS 2005, pp. 102–106 (2005)Google Scholar
  10. 10.
    Weiss, G.: Multiagent Systems. MIT Press, Cambridge (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tomoichi Takahashi
    • 1
  1. 1.Meijo University, TenpakuNagoyaJapan

Personalised recommendations