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.538. 智能系统学报 第11卷 2010,180(6):949-970. [9]LIN Guoping,LIANG Jiye,QIAN Yuhua,et al.A fuzzy [2]QIAN Yuhua,ZHANG Hu,SANG Yanli,et al.Multigranu- multigranulation decision-theoretic approach to multi-source lation decision-theoretic rough sets[].International journal fuzzy information systems[J].Knowledge-based systems, of approximate reasoning,2014,55(1):225-237. 2016,91:102-113. [3]QIAN Yuhua,LI Shunyong,LIANG Jiye,et al.Pessimistic [10]LIU Caihui,MIAO Duoqian,QIAN Jin.On multi-granula- rough set based decisions:a multigranulation fusion strategy tion covering rough sets[J].International journal of ap- [J].Information sciences,2014,264:196-210. proximate reasoning,2014,55(6):1404-1418. [4]LI Jinhai,REN Yue,MEI Changlin,et al.A comparative [11]别林斯里.概率与测度[M].3版.北京:世界图书出版 study of multigranulation rough sets and concept lattices via 公司,2007. rule acquisition[]]Knowledge-based systems,2016,91: [12]ZHU W,WANG Feiyue.Reduction and axiomization of 152-164 covering generalized rough sets[].Information sciences, [5]YANG Xibei,QI Yunsong,SONG Xiaoning,et al.Test cost 2003,152:217-230. sensitive multigranulation rough set:model and minimal cost [13]YAO Yiyu.Three-way decisions with probabilistic rough selection[].Information sciences,2013,250:184-199. sets[J].Information sciences,2010,180(3):341-353. [6]XU Weihua,GUO Yanting.Generalized multigranulation 作者简介: double-quantitative decision-theoretic rough set[J].Knowl- 刘财辉,男,1979年生,副教授,主 edge-based systems,2016,105:190-205. 要研究方向为粗糙集、粒计算、机器学 [7]SHE Yanhong,HE Xiaoli.On the structure of the multi- 习、数据挖掘。发表学术论文20余篇, granulation rough set model[].Knowledge-based systems, 其中被SCI检索4篇,EI检索12篇。 2012,36:81-92. [8]HUANG Bing,GUO Chunxiang,ZHUANG Yuliang,et al. 蔡克参,女,1992年生,硕士研究 Intuitionistic fuzzy multigranulation rough sets[].Informa- 生,主要研究方向为粒计算方法在农业 tion sciences,2014,277:299-320. 信息化中的应用。 2016国际脑与人工智能研讨会 International Workshop on Brain and Artificial Intelligence (BAI 2016) Creating human-level intelligent system is the long-standing mission for the field of Artificial Intelligence(AI)since its establishment nearly 60 years ago.Until now,however,there is still no general purpose intelligent system which can reach the human intelligence level in terms of coordinating various cognitive behaviors,adaptability of complex environ- ments,and autonomous learning under new environments.With the advancement of Brain Science,Neuroscience,and Cognitive Science,it is now possible for partially observing and obtaining data on the activities of brain neural networks at multiple scales while they are conducting various cognitive tasks.Hence,understandings of the brain at multiple scales will bring inspirations to future Artificial Intelligence research and applications.This workshop aims at bringing researchers and practitioners from Brain Science,Cognitive Science,Artificial Intelligence,etc.to discuss how they can collaborate and inspire each other to advance Brain-inspired Artificial Intelligence.The workshop will be co-located with the 2016 In- ternational Conference on Brain Informatics Health,October 13th,2016 in Omaha,Nebraska,USA. Website:http://bii.ia.ac.cn/bai-2016/2010, 180(6): 949-970. [ 2]QIAN Yuhua, ZHANG Hu, SANG Yanli, et al. Multigranu⁃ lation decision⁃theoretic rough sets[J]. International journal of approximate reasoning, 2014, 55(1): 225-237. [3]QIAN Yuhua, LI Shunyong, LIANG Jiye, et al. Pessimistic rough set based decisions: a multigranulation fusion strategy [J]. Information sciences, 2014, 264: 196-210. [4]LI Jinhai, REN Yue, MEI Changlin, et al. A comparative study of multigranulation rough sets and concept lattices via rule acquisition[ J]. Knowledge⁃based systems, 2016, 91: 152-164. [5]YANG Xibei, QI Yunsong, SONG Xiaoning, et al. Test cost sensitive multigranulation rough set: model and minimal cost selection[J]. Information sciences, 2013, 250: 184-199. [6] XU Weihua, GUO Yanting. Generalized multigranulation double⁃quantitative decision⁃theoretic rough set[J]. Knowl⁃ edge⁃based systems, 2016, 105: 190-205. [7] SHE Yanhong, HE Xiaoli. On the structure of the multi⁃ granulation rough set model[J]. Knowledge⁃based systems, 2012, 36: 81-92. [8]HUANG Bing, GUO Chunxiang, ZHUANG Yuliang, et al. Intuitionistic fuzzy multigranulation rough sets[ J]. Informa⁃ tion sciences, 2014, 277: 299-320. [9] LIN Guoping, LIANG Jiye, QIAN Yuhua, et al. A fuzzy multigranulation decision⁃theoretic approach to multi⁃source fuzzy information systems [ J]. Knowledge⁃based systems, 2016, 91: 102-113. [10]LIU Caihui, MIAO Duoqian, QIAN Jin. On multi⁃granula⁃ tion covering rough sets [ J]. International journal of ap⁃ proximate reasoning, 2014, 55(6): 1404-1418. [11]别林斯里. 概率与测度[M]. 3 版. 北京: 世界图书出版 公司, 2007. [12] ZHU W, WANG Feiyue. Reduction and axiomization of covering generalized rough sets[ J]. Information sciences, 2003, 152: 217-230. [13] YAO Yiyu. Three⁃way decisions with probabilistic rough sets[J]. Information sciences, 2010, 180(3): 341-353. 作者简介: 刘财辉,男,1979 年生,副教授,主 要研究方向为粗糙集、粒计算、机器学 习、数据挖掘。 发表学术论文 20 余篇, 其中被 SCI 检索 4 篇,EI 检索 12 篇。 蔡克参,女,1992 年生,硕士研究 生,主要研究方向为粒计算方法在农业 信息化中的应用。 2016 国际脑与人工智能研讨会 International Workshop on Brain and Artificial Intelligence (BAI 2016) Creating human⁃level intelligent system is the long⁃standing mission for the field of Artificial Intelligence (AI) since its establishment nearly 60 years ago. Until now, however, there is still no general purpose intelligent system which can reach the human intelligence level in terms of coordinating various cognitive behaviors, adaptability of complex environ⁃ ments, and autonomous learning under new environments. With the advancement of Brain Science, Neuroscience, and Cognitive Science, it is now possible for partially observing and obtaining data on the activities of brain neural networks at multiple scales while they are conducting various cognitive tasks. Hence, understandings of the brain at multiple scales will bring inspirations to future Artificial Intelligence research and applications. This workshop aims at bringing researchers and practitioners from Brain Science, Cognitive Science, Artificial Intelligence, etc. to discuss how they can collaborate and inspire each other to advance Brain⁃inspired Artificial Intelligence. The workshop will be co⁃located with the 2016 In⁃ ternational Conference on Brain Informatics & Health, October 13th, 2016 in Omaha, Nebraska, USA. Website:http: / / bii.ia.ac.cn / bai⁃2016 / ·538· 智 能 系 统 学 报 第 11 卷
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