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

Authors:
Ivette C. Martínez;David Ojeda;Ezequiel A. Zamora
Affiliations:
Grupo de Inteligencia Artificial, Universidad Simón Bolívar, Caracas 1080-A, Venezuela;Grupo de Inteligencia Artificial, Universidad Simón Bolívar, Caracas 1080-A, Venezuela;Grupo de Inteligencia Artificial, Universidad Simón Bolívar, Caracas 1080-A, Venezuela
Venue:
RoboCup 2006: Robot Soccer World Cup X
Year:
2006

Citing 2
Cited 2

Classifier fitness based on accuracy

Evolutionary Computation
Reinforcement learning: a survey

Journal of Artificial Intelligence Research

Decision and coordination strategies for robocup rescue agents

SIMPAR'10 Proceedings of the Second international conference on Simulation, modeling, and programming for autonomous robots
A reinforcement learning based method for optimizing the process of decision making in fire brigade agents

EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

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.