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arXiv 1707.04828cs.AI
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基于FML的围棋人机协作系统动态评估Agent

FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

National University of Tainan(台南大学);Tokyo Metropolitan University(东京都立大学);Haifong Weiqi Academy(海峰围棋学院)

Chang-Shing Lee, Mei-Hui Wang, Sheng-Chi Yang, Pi-Hsia Hung, Su-Wei Lin, Nan Shuo, Naoyuki Kubota, Chun-Hsun Chou, Ping-Chiang Chou, Chia-Hsiu Kao

摘要:

在本文中,我们展示了应用模糊置标语言(FML)构建一个基于FML的动态评估代理(FDAA),我们提出了一个基于FML的人机合作系统(FHMCS)的围棋比赛。提出的FDAA包括一个智能决策和学习机制,智能游戏机器人,近端开发代理,和智能代理。智能游戏机器人基于Facebook Darkforest的开源代码,它具有代表性的状态转移应用程序编程接口机制。最接近的开发代理包含一个动态评估机制,GoSocket机制,和一个模糊知识库和规则库的FML引擎。智能代理包含一个GoSocket引擎和一个汇总代理,该汇总代理基于估计的胜率、实时模拟数量和预测移动的匹配度。此外,还给出了用于玩家性能评估的FML和用于游戏结果评论的语言描述.我们在2016年测试了5场比赛,并在2017年1月测试了60场Google Master Go(AlphaGo程序的新版本)的比赛,通过实验验证和验证了FDAA和FHMCS变体的性能。实验结果表明,FDAA算法可以有效地应用于Go语言的应用程序。

英文摘要:

In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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