期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于TrueSkill模型的围棋棋手排名方法及评估 被引量：6

A Ranking Approach of Go Players Based on TrueSkill Model with Empirical Evaluation
在线阅读 下载PDF
导出
摘要 如何根据比赛结果自动地估计参赛选手的真实竞技水平,是现代体育排名算法的核心研究内容.本文在综合分析现有等级分制、Elo算法和TrueSkill算法的基础上,通过仿真数据和真实数据,对比分析了TrueSkill排名、Elo排名和棋手段位间的差异及其原因.并且从排名的客观性、时效性、稳定性以及准确性四个方面,对TrueSkill算法的排名结果进行实际应用的可行性分析.实验结果表明TrueSkill的排名结果有较好的客观性、时效性、稳定性以及准确性,在仿真数据中TrueSkill的排名比Elo更合理. How to estimate player skill levels from game results is a key part of modern ranking algorithms for sports events. This paper firstly compares and analyzes Elo algorithm, TrueSkill algorithm, and the existing grad- ing system used in Go community. Simulation data and actual game results are then employed to generate ranking results according to these methods. Possible reasons for the differences are also offered. A feasibility analysis of using TrueSkill algorithm in real world practice is carried out by using objectivity, respond delay, stability, and accuracy as the rating criteria. Experiments show that rankings generated by TrueSkill algorithm have good prop- erties and it outperforms Elo in simulations by giving more sensible rankings.
作者 吴霖 陈磊 邓超 袁梅宇 江虹
机构地区 昆明理工大学信息工程与自动化学院
出处 《昆明理工大学学报（自然科学版）》 CAS 北大核心 2013年第3期47-55,共9页 Journal of Kunming University of Science and Technology(Natural Science)
基金 国家自然科学基金(61163004) 云南省应用基础研究面上项目(2010CD027)
关键词 评分系统 围棋 贝叶斯 TrueSkill rating system Go Bayesian TrueSkill
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献13

  • 1中国围棋竞赛规则(2002年版)[EB/OL].[2012—10—16].http://games.sports.cn/datebase/encyelopaedia/wq/2010—07—19/2046610_3.html.
  • 2Taeil Bai. The New Rating System of the Korea Baduk Association (EB/OL).[2012 - 11 - 12]. http://english. baduk. or. kr/sub03_04 -1. htm? menu = f13&divL =6.
  • 3Herbrich R, Minka T, Graepel T. TrueSkill (TM) : A Bayesian skill rating system[C] I lIn Advances in Neural Information Pro?cessing Systems 20. Scholkopf B, PlattJ, Hoffman T. Cambridge: MIT Press, 2007 :569 - 576.
  • 4Zhang Xinhua, Graepel T, Ralf Herbrich. Bayesian Online Learning for Multi - label and Multi - variate Performance Meas?ures[C] II Proceedings of the 13 th International Conference on Artificial Intelligence and Statistics (AIST ATS) , 2010. Yee Whye The, Mike Titterington. Brookline: Microtome Publishing, 2010:956 -963.
  • 5Graepel T, CandelaJ Q, Borchert T, Herbrich R. Web - scale Bayesian click - through rate prediction for sponsored search advertising in microsoft's bing search engine[C] IIProceedings of the 27th International Conference on Machine Learning IC?ML 2010.Johannes Furnkranz , ThorstenJoachims. Anderson: Omnipress , 2010: 13 - 20.
  • 6RANKINGS (EB/OL).[2012 -10 -15]. http://www.atpworldtour.comiRankings/Rankings - FAQ. aspx.
  • 7Elo A E. The new U. S. C. F. rating system[J]. Chess Life, 1961,16: 160 -161.
  • 8Elo A E. The Rating of Chess Players, Past and Present[M]. New York: Areo Publishing, 1978.
  • 9Clickman M E. A Comprehensive guide to chess ratings[J]. Amer. ChessJournal, 1995 (3) :59 - 102.
  • 10Minka T P. Expectation propagation for approximate Bayesian inference[CJ II Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence.John Breese, Daphne Koller. San Francisco: Morgan Kaufmann Publishers Inc. , 2001 : 362 -369.

二级参考文献4

共引文献22

同被引文献40

  • 1李珊英.以问卷方式进行民意调查的局限性[J].国际新闻界,1988,10(2):27-30. 被引量：1
  • 2ELO A E. The rating of chess players, past and present [M]. New York: Arco Publishing, 1978.
  • 3HERBRICH R, MINKA T, GRAEPEL T. TrueSkill: a Bayesian skill rating system [C] // Proceedings of Advances in Neural Information Processing Systems 19. Cambridge: MIT Press, 2007:569-576.
  • 4STERN D, HERBRICH R, GRAEPEL T. Bayesian pattern ranking for move prediction in the game of Go [C] // Proceedings of the 23rd International Conference on Machine Learning. New York: ACM Press, 2006: 873-880.
  • 5WISTUBA M, SCHAEFERS L, PLATZNER M. Comparison of Bayesian move prediction systems for Computer Go [C] // Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games. Piscataway: IEEE Press, 2012: 91-99.
  • 6GRAEPEL T, CANDELA J Q, BORCHERT T, et al. Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft's bing search engine [C] // Proceedings of the 27th International Conference on Machine Learning. Anderson: Omnipress, 2010:13-20.
  • 7KIM S, QIN T, LIU T Y, et al. Advertiser-centric approach to understand user click behavior in sponsored search [J]. Information Sciences, 2014, 276: 242-254.
  • 8ZHANG X, GRAEPEL T, HERBRICH R. Bayesian online learning for multi-label and multi-variate performance measures [C] // Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. Redmond, Washington, USA: Microsoft Press, 2010:956-963.
  • 9LEE J S. TrueSkill-based pairwise coupling for multi-class classification [C] // Proceedings of the 22nd International Conference on Artificial Neural Networks and Machine Learning, LNCS 7553. Berlin: Springer-Verlag, 2012: 213-220.
  • 10HUANG J, ZIMMERMANN T, NAGAPAN N, et al. Mastering the art of war: how patterns of gameplay influence skill in Halo [C] // Proceedings of the 2013 International Conference on Human Factors in Computing Systems. New York: ACM Press, 2013: 695-704.

引证文献6

二级引证文献3

昆明理工大学学报（自然科学版）
2013年 第3期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部