Interactive Learning of Expert Criteria for Rescue Simulations
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies
Autonomous Agents and Multi-Agent Systems
Learning parametrised RoboCup rescue agent behaviour using an evolutionary algorithm
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Decision and coordination strategies for robocup rescue agents
SIMPAR'10 Proceedings of the Second international conference on Simulation, modeling, and programming for autonomous robots
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
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A fundamental difficulty faced by cooperative multiagent systems is to find how to efficiently coordinate agents. There are three fundamental processes to solve the coordination problem: mutual adjustment, direct supervision and standardization. In this paper, we present our results, obtained in the RoboCupRescue environment, comparing those coordination approaches to find which one is the best for a complex real-time problem like this one. Our results show that a decentralized approach based on mutual adjustment can be more flexible and give better results than a centralized approach using direct supervision. Also, we have obtained results showing that a standardization rule like the partitioning of the map can be helpful in those kind of environments.