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首页> 外文会议>IEEE International Conference on Fuzzy Systems >AI-FML Agent for Robotic Game of Go and AIoT Real-World Co-Learning Applications
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AI-FML Agent for Robotic Game of Go and AIoT Real-World Co-Learning Applications

机译：适用于围棋机器人游戏和AIoT现实世界共同学习应用程序的AI-FML代理

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In this paper, we propose an AI-FML agent for robotic game of Go and AIoT real-world co-learning applications. The fuzzy machine learning mechanisms are adopted in the proposed model, including fuzzy markup language (FML)-based genetic learning (GFML), eXtreme Gradient Boost (XGBoost), and a seven-layered deep fuzzy neural network (DFNN) with backpropagation learning, to predict the win rate of the game of Go as Black or White. This paper uses Google AlphaGo Master sixty games as the dataset to evaluate the performance of the fuzzy machine learning, and the desired output dataset were predicted by Facebook AI Research (FAIR) ELF Open Go AI bot. In addition, we use IEEE 1855 standard for FML to describe the knowledge base and rule base of the Open Go Darkforest (OGD) prediction platform in order to infer the win rate of the game. Next, the proposed AI-FML agent publishes the inferred result to communicate with the robot Kebbi Air based on MQTT protocol to achieve the goal of human and smart machine co-learning. From Sept. 2019 to Jan. 2020, we introduced the AI-FML agent into the teaching and learning fields in Taiwan. The experimental results show the robots and students can co-learn AI tools and FML applications effectively. In addition, XGBoost outperforms the other machine learning methods but DFNN has the most obvious progress after learning. In the future, we hope to deploy the AI-FML agent to more available robot and human co-learning platforms through the established AI-FML International Academy in the world.
机译：在本文中，我们提出了一种用于Go和AIoT现实世界共同学习应用程序的机器人游戏的AI-FML代理。所提出的模型采用了模糊机器学习机制，包括基于模糊标记语言（FML）的遗传学习（GFML），eXtreme Gradient Boost（XGBoost）和具有反向传播学习的七层深度模糊神经网络（DFNN），预测Go（黑白）游戏的获胜率。本文使用Google AlphaGo Master 60游戏作为数据集来评估模糊机器学习的性能，并通过Facebook AI Research（FAIR）ELF Open Go AI机器人预测了所需的输出数据集。此外，我们使用FML的IEEE 1855标准来描述Open Go Darkforest（OGD）预测平台的知识库和规则库，以便推断游戏的获胜率。接下来，拟议的AI-FML代理发布推断的结果，以基于MQTT协议与机器人Kebbi Air进行通信，从而实现人机和智能机共同学习的目标。从2019年9月到2020年1月，我们将AI-FML代理引入了台湾的教学领域。实验结果表明，机器人和学生可以有效地共同学习AI工具和FML应用程序。此外，XGBoost优于其他机器学习方法，但DFNN在学习后的进步最为明显。将来，我们希望通过全球已建立的AI-FML国际学院，将AI-FML代理部署到更多可用的机器人和人工学习平台上。

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