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Evolutionary construction of behavior arbitration mechanisms based on dynamically-rearranging neural networks

机译:基于动态重排神经网络的行为仲裁机制的进化构建

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Recently, the evolutionary robotics (ER) approach has been attracting lots of concern in the fields of robotics and artificial life, since it can automatically synthesize controllers by taking the embodiment and the interaction dynamics between the robot and its environment. However, the ER approach still has serious problems that have to be solved. In this study, we particularly focus on one of the critical problems in the ER: plasticity vs. stability dilemma. In order to alleviate this problem, we investigate the effectiveness of the dynamically-rearranging neural networks by taking a peg-collecting task, which requires appropriate sequence of behavior to accomplish the task, as a practical example.
机译:近年来,进化机器人技术(ER)方法在机器人技术和人工生活领域引起了很多关注,因为它可以通过采用机器人及其周围环境之间的实施方式和交互动力学来自动合成控制器。但是,ER方法仍存在必须解决的严重问题。在这项研究中,我们特别关注ER中的关键问题之一:可塑性与稳定性难题。为了减轻这个问题,我们以钉子收集任务为例,研究了动态重新排列的神经网络的有效性,该任务需要适当的行为顺序才能完成任务。

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