1 引言在决策过程中,如何处理不确定性,已吸引了不少学者的注意,对此,人们提出了各种各样关于不确定性信息的表示模型,及不确定性推理方法,如Shortliffe和Buchanan的不确定因子法,Duda的概率逻辑,Dempster和shafer提出的证据理论方法,Zadeh的模糊集理论,Dubois和Prade提出的可能度推理方法等。近年来,波兰人Pawlak建立的粗集理论在学术界影响很大,这是因为粗集理论为数据挖掘和知识发现领 1 Introduction In the decision-making process, how to deal with the uncertainty has attracted the attention of many scholars, on which people put forward a variety of models for the expression of uncertainty and uncertainty reasoning methods such as Shortliffe And Buchanan’s Uncertainty Factor Method, Duda’s Probability Logic, Evidence Theory Methods Dempster and Shafer, Zadeh’s Fuzzy Sets Theory, Dubois and Prade’s Probability Inference Methods. In recent years, the theory of rough set established by Polish Pawlak has great influence in academia because of the theory of rough set for data mining and knowledge discovery