不确定信息的粗糙集表示和处理

随机性和模糊性是不确定性中最重要和最基本的2个特征。分析和比较了表示和处理不确定性知识的一些主要的拓展集合理论，并系统的介绍了基于粗糙集的不确定知识的表示和处理方法。系统讨论了粗糙集理论对经典集合论的拓展，用经典集合计算方法对粗糙集的核心算子进行了对比分析，同时对定义在信息系统上的粗糙逻辑也进行了分析。通过分析粗糙集理论在人工智能领域的几类典型应用案例说明了粗糙集在表示和处理不确定性问题方面的重要作用和优势。最后对不确定知识的表示和处理的一些有待进一步深入研究的关键问题进行了展望。

Uncertain knowledge representation and processing based on rough set

The randomness and fuzziness are the most important and basic for the uncertainty characteristics. Some major expansion of set theory about uncertain knowledge representation and processing are analyzed and compared in this paper. There is a systematic introduction of uncertainty based on rough set knowledge representation and processing methods. Extension of rough set theory based on the classical set theory is discussed deeply. Core operators of rough set were analyzed based on the classical set theory. At the same time, rough logic defined on the information systems is analyzed. It is proved that the important role and advantages of rough set theory for representing and dealing with the uncertainty based on the typical cases in the field of artificial intelligence. Finally, key issues about further studying on uncertain knowledge representation and processing are also discussed.