ABSTRACT
The DEFACTO system is a multiagent based tool for training incident commanders for large scale disasters. In this paper, we highlight some of the lessons that we have learned from our interaction with the Los Angeles Fire Department (LAFD) and how they have affected the way that we continued the design of our training system. These lessons were gleaned from LAFD feedback and initial training exercises and they include: system design, visualization, improving trainee situational awareness, adjusting training level of difficulty and situation scale. We have taken these lessons and used them to improve the DEFACTO system's training capabilities. We have conducted initial training exercises to illustrate the utility of the system in terms of providing useful feedback to the trainee.
- J. W. Baxter and G. S. Horn. Controlling teams of uninhabited air vehicles. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems (AAMAS), 2005. Google ScholarDigital Library
- S. Karim and C. Heinze. Experiences with the design and implementation of an agent-based autonomous uav controller. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems (AAMAS), 2005. Google ScholarDigital Library
- H. Kitano, S. Tadokoro, I. Noda, H. Matsubara, T. Takahashi, A. Shinjoh, and S. Shimada. Robocup rescue: Search and rescue in large-scale disasters as a domain for autonomous agents research. In IEEE SMC, volume VI, pages 739--743, Tokyo, October 1999.Google ScholarCross Ref
- L. L. N. Laboratory. Jcats - joint conflict and tactical simulation. In http://www.jfcom.mil/about/fact_jcats.htm, 2005.Google Scholar
- D. V. Pynadath and M. Tambe. Automated teamwork among heterogeneous software agents and humans. Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 7:71--100, 2003. Google ScholarDigital Library
- P. Scerri, D. Pynadath, and M. Tambe. Towards adjustable autonomy for the real world. Journal of Artificial Intelligence Research, 17:171--228, 2002. Google ScholarCross Ref
- P. Scerri, D. V. Pynadath, L. Johnson, P. Rosenbloom, N. Schurr, M. Si, and M. Tambe. A prototype infrastructure for distributed robot-agent-person teams. In AAMAS, 2003. Google ScholarDigital Library
- N. Schurr, J. Marecki, P. Scerri, J. P. Lewis, and M. Tambe. The defacto system: Training tool for incident commanders. In The Seventeenth Innovative Applications of Artificial Intelligence Conference (IAAI), 2005. Google ScholarDigital Library
- A. S. Technology. Epics - emergency preparedness incident commander simulation. In http://epics.astcorp.com, 2005.Google Scholar
- W. A. van Doesburg, A. Heuvelink, and E. L. van den Broek. Tacop: A cognitive agent for a naval training simulation environment. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems (AAMAS), 2005. Google ScholarDigital Library
Index Terms
- Using multiagent teams to improve the training of incident commanders
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