Using Multi-Agent Teams to Improve the Training of Incident Commanders
The DEFACTO system is a multi-agent based tool for training incident commanders for large scale disasters. While this system is currently used for the command of a disaster response scenario, the lessons learned and the methods used to approach this challenging domain apply directly to military applications such as the command and control of troops. 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.
KeywordsTraining Exercise Multiagent System Autonomous Agent Team Performance Disaster Response
Unable to display preview. Download preview PDF.
- 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 Scholar
- 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 Scholar
- 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 Scholar
- L. L. N. Laboratory, “Jcats-joint conflict and tactical simulation,” in http://www.jfcom.mil/about/fact jcats.htm, 2005.Google Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar