Modal reasoning for uncertain information in expert system

Uncertainty information is in many information processing systems, such as data integration system, and expert systems, and so on. There is a contradiction, reasoning detailed information on system requirements can be the most accurate results, while the expert system input is uncertain. So how to reason using uncertain information, and get good results, is our main concern, but also the field of expert systems, one of the core issues. Reasoning with uncertain information is a problem of key importance when dealing with real knowledge. We propose rough logic as a foundation for approximate reasoning about rule-based complex objects. The theory of rough sets is not information intensive and is thus a good basis for reasoning in domains where knowledge is sparse. We are concerned with formal models of reasoning under uncertainty, then we present a logic based on rough set theory that is suitable for reasoning under uncertainty, a rough inference rule, and demonstrate its effectiveness in rule-based reasoning.