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Introduction of Acquiring Method for Agents' Actions with Simple Ant Colony Optimization in RoboCup Rescue Simulation System

机译:在RoboCup救援模拟系统中引入简单蚁群优化的代理人行为获取方法

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This paper has presented acquiring method for agents' actions using Ant Colony Optimization (ACO) in multi-agent system. ACO is one of powerful meta-heuristics algorithms and some researchers have reported the effectiveness of some applications with the algorithm [1-4]. I have developed fire brigade agents using proposed method in RoboCup rescue simulation system. The final goal of this research is an achievement of co-operations for hetero-agent in multi-agent systems. Then this research for implementation for fire brigade agents in my team is the first step of this goal.
机译:提出了一种在多智能体系统中使用蚁群优化算法获取智能体行为的方法。 ACO是强大的元启发式算法之一,一些研究人员报告了该算法在某些应用中的有效性[1-4]。我使用RoboCup救援模拟系统中提出的方法开发了消防员代理。这项研究的最终目标是在多智能体系统中实现异构智能体的合作。然后,对我的团队中的消防官进行实施的研究是该目标的第一步。

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