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Ambulance Decision Support Using Evolutionary Reinforcement Learning in Robocup Rescue Simulation League

  • Ivette C. Martínez
  • David Ojeda
  • Ezequiel A. Zamora
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4434)

Abstract

We present a complete design of agents for the RoboCup Rescue Simulation problem that uses an evolutionary reinforcement learning mechanism called XCS, a version of Holland’s Genetic Classifiers Systems, to decide the number of ambulances required to rescue a buried civilian. We also analyze the problems implied by the rescue simulation and present solutions for every identified sub-problem using multi-agent cooperation and coordination built over a subsumption architecture. Our agents’ classifier systems were trained in different disaster situations. Trained agents outperformed untrained agents and most participants of the 2004 RoboCup Rescue Simulation League competition. This system managed to extract general rules that could be applied on new disaster situations, with a computational cost of a reactive rule system.

Keywords

Alive Agent Disaster Situation Evolutionary Reinforcement System Genetic Program Agent Team 
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

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ivette C. Martínez
    • 1
  • David Ojeda
    • 1
  • Ezequiel A. Zamora
    • 1
  1. 1.Grupo de Inteligencia Artificial, Universidad Simón Bolívar, Caracas 1080-AVenezuela

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