In this paper we propose to model of a knowledge-based expert systems using the techniques of Rough Sets approach. In the real world problem solving is the process of finding a solution when the path leading to that solution is uncertain. The Expert Systems need to have the ability to handle vague associations, for example by accepting the degree of correlations as numerical certainty factors. When the data is incomplete or missing, the only solution is to accept the value "unknown" and proceed to an approximate reasoning with this missing value. Rough sets provides a framework to model uncertainity, the human way of thinking, reasoning and the perception process. To run an expert system, the engine reasons about the knowledge base like a human. In the 80's a third part appeared: a dialog interface to communicate with users. This ability to conduct a conversation with users was later called "conversational". Rough set theory is a technique deals with uncertainty.
Knowledge based systems are systems that are designed to emulate human thinking to solve problems and provide advices. One kind of knowledge based systems is Expert System. Although it is widely used in various applications, such systems are not able to model real world problems which are full of ambiguities and vagueness. When Rough Set theory was introduced by I. Pawlak at 1991, it did not get the attention of expert system's researchers. According to Pawlak ," a formal approximation of a crisp set (i.e., conventional set) in terms of a pair of sets which give the lower and the upper approximation of the original set.". The idea of rough set was to show that there is a world behind conventional logic. This kind of logic is the proper way to model human thinking. The expert system that uses a collection of rough sets and rules to facilitate reasoning is called a knowledge-based Expert System.
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