The currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that human-robot cooperation is of special importance. Various types of robots have been applied to the so-called field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzy-based system for a human-robot cooperative Edutainment. This co-learning system includes a brain-computer interface (BCI) ontology model and a Fuzzy Markup Language (FML)-based Reinforcement Learning Agent (FRL-Agent). The proposed FRL-Agent is composed of (1) a human learning agent, (2) a robotic teaching agent, (3) a Bayesian estimation agent, (4) a robotic BCI agent, (5) a fuzzy machine learning agent, and (6) a fuzzy BCI ontology. In order to verify the effectiveness of the proposed system, the FRL-Agent is used as a robot teacher in a number of elementary schools, junior high schools, and at a university to allow robot teachers and students to learn together in the classroom. The participated students use handheld devices to indirectly or directly interact with the robot teachers to learn English. Additionally, a number of university students wear a commercial EEG device with eight electrode channels to learn English and listen to music. In the experiments, the robotic BCI agent analyzes the collected signals from the EEG device and transforms them into five physiological indices when the students are learning or listening. The Bayesian estimation agent and fuzzy machine learning agent optimize the parameters of the FRL agent and store them in the fuzzy BCI ontology. The experimental results show that the robot teachers motivate students to learn and stimulate their progress. The fuzzy machine learning agent is able to predict the five physiological indices based on the eight-channel EEG data and the trained model. In addition, we also train the model to predict the other students’ feelings based on the analyzed physiological indices and labeled feelings. The FRL agent is able to provide personalized learning content based on the developed human and robot cooperative edutainment approaches. To our knowledge, the FRL agent has not applied to the teaching fields such as elementary schools before and it opens up a promising new line of research in human and robot co-learning. In the future, we hope the FRL agent will solve such an existing problem in the classroom that the high-performing students feel the learning contents are too simple to motivate their learning or the low-performing students are unable to keep up with the learning progress to choose to give up learning.

中文翻译：

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具有模糊本体的基于 FML 的强化学习代理用于人机协作教育

目前观察到的人工智能 (AI) 发展及其对不同类型行业的影响意味着人机合作尤为重要。各种类型的机器人已经应用于所谓的教育娱乐领域，即教育与娱乐相结合的领域。本文介绍了一种新颖的基于模糊的人机协作教育系统。该协同学习系统包括脑机接口 (BCI) 本体模型和基于模糊标记语言 (FML) 的强化学习代理 (FRL-Agent)。所提出的 FRL-Agent 由 (1) 人类学习代理、(2) 机器人教学代理、(3) 贝叶斯估计代理、(4) 机器人 BCI 代理、(5) 模糊机器学习代理和(6)模糊BCI本体。为了验证所提出系统的有效性，FRL-Agent 在许多小学、初中和大学中用作机器人教师，让机器人教师和学生在课堂上一起学习。参与的学生使用手持设备间接或直接与机器人教师互动学习英语。此外，一些大学生佩戴带有八个电极通道的商用脑电图设备来学习英语和听音乐。在实验中，机器人 BCI 代理会分析从 EEG 设备收集到的信号，并在学生学习或聆听时将其转换为五个生理指标。贝叶斯估计代理和模糊机器学习代理优化 FRL 代理的参数并将它们存储在模糊 BCI 本体中。实验结果表明，机器人教师能激励学生学习并激发他们的进步。模糊机器学习代理能够根据八通道脑电图数据和训练模型预测五个生理指标。此外，我们还训练模型根据分析的生理指标和标记的感受来预测其他学生的感受。FRL 代理能够基于开发的人机协作教育娱乐方法提供个性化的学习内容。据我们所知，FRL 智能体之前还没有应用于小学等教学领域，它为人机协同学习开辟了一条有前途的新研究方向。将来，