基于DFL的模糊缺省推理研究

DFL-Based Fuzzy Default Inference

统计缺省理论是经典缺省逻辑(Reiter缺省)的推广,借助错误参数ε,允许我们在标准的推理统计中模型化普遍的推理模式。本文针对研究对象以及它们之间的动态模糊性,提出了基于动态模糊逻辑(DFL)的模糊缺省推理,并通过算子语义的方法计算模糊缺省扩充。

Statistical default logic is an expansion of the classic（i, e. Reiter＇s） default logic that allows us to model common inference patterns found in standard inferential statistics, which is an expansion with an error-parameter ε. This paper proposes the fuzzy default inference based on DFL and gives a method of constructing the extensions of the fuzzy default theory.