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人工智能论文:基于PFML的语义BCI AGENT用于GO学习和预测游戏(PFML-based Semantic BCI Agent for Game of G

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按梦儿 发表于 2019-1-11 11:27:34 | 显示全部楼层 |阅读模式
按梦儿 2019-1-11 11:27:34 735 0 显示全部楼层
人工智能论文:基于PFML的语义BCI AGENT用于GO学习和预测游戏(PFML-based Semantic BCI Agent for Game of Go Learning and Prediction)本文提出了一种语义脑计算机接口(BCI)代理,基于模糊标记语言(FML)的粒子群优化算法(PSO),用于学习和预测应用。此外,我们还通过Facebook AI研究(FAIR)开源Darkark和ELF OpenGo AI机器人建立了OpenGo Darkforest(OGD)云平台。日本机器人Palro将同时预测棋盘游戏Go to the Go玩家的移动优势,以供参考学习。所提出的语义BCI代理通过来自其脑波的基于人的BCI数据和来自OGD云平台的预测的基于机器的游戏数据来有效地操作,以优化人与机器之间的参数。实验结果表明,所提出的人机智能机器共同学习机制表现良好。我们希望为学生提供更好的在线学习环境,结合各种手持设备,机器人或计算机设备,以实现未来所需的智能学习目标。
This paper presents a semantic brain computer interface (BCI) agent withparticle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Golearning and prediction applications.Additionally, we also establish an OpenGo Darkforest (OGD) cloud platform with Facebook AI research (FAIR) open sourceDarkforest and ELF OpenGo AI bots.The Japanese robot Palro will simultaneouslypredict the move advantage in the board game Go to the Go players for referenceor learning.The proposed semantic BCI agent operates efficiently by thehuman-based BCI data from their brain waves and machine-based game data fromthe prediction of the OGD cloud platform for optimizing the parameters betweenhumans and machines.Experimental results show that the proposed human andsmart machine co-learning mechanism performs favorably.We hope to providestudents with a better online learning environment, combining different kindsof handheld devices, robots, or computer equipment, to achieve a desired andintellectual learning goal in the future.人工智能论文:基于PFML的语义BCI AGENT用于GO学习和预测游戏(PFML-based Semantic BCI Agent for Game of Go Learning and Prediction) axubn3F4LxlNbP6P.jpg
URL地址:https://arxiv.org/abs/1901.02999     ----pdf下载地址:https://arxiv.org/pdf/1901.02999    ----人工智能论文:基于PFML的语义BCI AGENT用于GO学习和预测游戏(PFML-based Semantic BCI Agent for Game of Go Learning and Prediction)
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