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首页> 外文期刊>Artificial life and robotics >Evaluation-function modeling with multi-layered perceptron for RoboCup soccer 2D simulation
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Evaluation-function modeling with multi-layered perceptron for RoboCup soccer 2D simulation

机译：用于Robocup足球2D模拟的多层Perceptron评估功能建模

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摘要

In the RoboCup soccer simulation 2D league, players make a decision at each cycle in real time. The performance of a team highly depends on the agents' decision-making process, which is composed of a action planning method and an evaluation function of the soccer field. In this work, a cooperative action planning based on the tree search is employed. Each action is evaluated by an evaluation function. We employ a multi-layered perceptron to construct an evaluation function. We examine the performance of the soccer agents when various sets of features are used as the input of the neural network. A feature vector is made of kick sequences executed by an expert team extracted from log files. To investigate the efficiency of our approach, we compare the performance of a team using an evaluation function modeled by neural networks against a team using a hand-tuned evaluation function.
机译：在Robocup足球仿真2D联赛中，玩家实时在每个周期作出决定。团队的表现高度取决于代理商的决策过程，该过程由行动规划方法和足球场的评估函数组成。在这项工作中，采用基于树搜索的合作行动规划。每个动作由评估函数评估。我们采用多层的Perceptron来构建评估功能。当各种特征用作神经网络的输入时，我们检查足球代理的性能。特征向量由由日志文件中提取的专家团队执行的踢序列组成。为了调查我们的方法的效率，我们使用由手术调整评估功能使用神经网络模型的评估函数来比较团队的性能。

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