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Using Multi-Agent Teams to Improve the Training of Incident Commanders

  • Nathan Schurr
  • Milind Tambe
Part of the Whitestein Series in Software Agent Technologies and Autonomic Computing book series (WSSAT)

Abstract

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.

Keywords

Training Exercise Multiagent System Autonomous Agent Team Performance Disaster Response 
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.

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Copyright information

© Birkhäuser Verlag Basel/Switzerland 2007

Authors and Affiliations

  • Nathan Schurr
    • 1
  • Milind Tambe
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA

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