{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fml-based-dynamic-assessment-agent-for-human","title":"FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go","arxiv_id":"1707.04828","date":"2017-07-16","proceeding":null,"authors":["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"],"abstract":"In this paper, we demonstrate the application of Fuzzy Markup Language (FML)\nto construct an FML-based Dynamic Assessment Agent (FDAA), and we present an\nFML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The\nproposed FDAA comprises an intelligent decision-making and learning mechanism,\nan intelligent game bot, a proximal development agent, and an intelligent\nagent. The intelligent game bot is based on the open-source code of Facebook\nDarkforest, and it features a representational state transfer application\nprogramming interface mechanism. The proximal development agent contains a\ndynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a\nfuzzy knowledge base and rule base. The intelligent agent contains a GoSocket\nengine and a summarization agent that is based on the estimated win rate,\nreal-time simulation number, and matching degree of predicted moves.\nAdditionally, the FML for player performance evaluation and linguistic\ndescriptions for game results commentary are presented. We experimentally\nverify and validate the performance of the FDAA and variants of the FHMCS by\ntesting five games in 2016 and 60 games of Google Master Go, a new version of\nthe AlphaGo program, in January 2017. The experimental results demonstrate that\nthe proposed FDAA can work effectively for Go applications.","url_abs":"http://arxiv.org/abs/1707.04828v1","url_pdf":"http://arxiv.org/pdf/1707.04828v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fml-based-dynamic-assessment-agent-for-human","repo_url":"https://github.com/CI-labo-OPU/FML_Competition2020","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"game-of-go","task_name":"Game of Go"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}