Fuzzy Inference Model for Learning from Experiences and Its Application to Robot Navigation
Fuzzy Inference Model for Learning from Experiences and Its Application to Robot Navigation
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经验学习的模糊推理模型及其在机器人导航中的应用
DOI：
10.1109/cimca.2005.1631325
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
H. Aso
中科院分区：
文献类型：
--
作者：
M. Gouko;Yoshihiro Sugaya;H. Aso
提出了一种从经验中学习的模糊推理模型（FILE）。该模型可以从任务试错获得的经验数据中学习，并且可以稳定地从尝试的成功和失败的经验中学习。模型的学习是在每次任务尝试之后执行的。因此，预计成功率会随着试验的重复而增加，并且模型会适应环境的变化。在本文中，我们通过将模型应用于机器人导航任务模拟来确认模型的性能，并研究通过学习获得的知识
A fuzzy inference model for learning from experiences (FILE) is proposed. the model can learn from experience data obtained by trial-and-error of a task and it can stably learn from both experiences of success and failure of a trial. the learning of the model is executed after each of trial of the task. hence, it is expected that the achievement rate increases with repetition of the trials, and that the model adapts to change of environment. in this paper, we confirm performance of the model by applying the model to a robot navigation task simulation and investigate the knowledge acquired by the learning