应用遗传算法和模糊置标语言构建了一个围棋人机协作学习系统。基于遗传模糊置标语言（GFML）的机器人Agent可以在各种机器人上工作，包括Palro、Pepper和TMUs机器人。我们使用FAIR开源Darkforest和OpenGo AI机器人的参数来构建Open Go Darkforest（OGD）云平台的知识库，用于学生在互联网上学习。此外，我们采用AlphaGo Master 60场在线对弈的数据作为训练数据，构建了协同学习系统的知识库和规则库。首先，Darkforest根据OGD平台上每个游戏的各种模拟数和匹配率预测胜率，然后OpenGo的胜率作为最终的期望输出。实验结果表明，该方法可以提高Darkforest和OpenGo人工智能机器人在不同模拟次数下的预测能力。
英文摘要：
This paper applies a genetic algorithm and fuzzy markup language to construct a human and smart machine cooperative learning system on game of Go. The genetic fuzzy markup language (GFML)-based Robot Agent can work on various kinds of robots, including Palro, Pepper, and TMUs robots. We use the parameters of FAIR open source Darkforest and OpenGo AI bots to construct the knowledge base of Open Go Darkforest (OGD) cloud platform for student learning on the Internet. In addition, we adopt the data from AlphaGo Master sixty online games as the training data to construct the knowledge base and rule base of the co-learning system. First, the Darkforest predicts the win rate based on various simulation numbers and matching rates for each game on OGD platform, then the win rate of OpenGo is as the final desired output. The experimental results show that the proposed approach can improve knowledge base and rule base of the prediction ability based on Darkforest and OpenGo AI bot with various simulation numbers.
Exit-and-Join Dynamics and Equilibrium in Continuum Cooperative Games
Department of Electrical and Computer Engineering（电气与计算机工程系）；New York University Tandon School of Engineering（纽约大学塔恩工程学院）；Brooklyn, NY, USA（布鲁克林，纽约，美国）
Cooperative Game-Theoretic Credit Assignment for Multi-Agent Policy Gradients via the Core
Key Laboratory of Intelligence, Games and Information Processing in Shaanxi Province（陕西省智能信息处理重点实验室）；Unmanned System Research Institute（无人系统研究院）；Northwestern Polytechnical University（西北工业大学）；School of Mathematics and Statistics（数学与统计学院）；Information Science Academy（信息科学学院）；China Electronics Technology Group Corporation（中国电子科技集团有限公司）
