Knowledge acquisition, incremental modification and reasoning based on rough set theory in expert systems

Knowledge acquisition, incremental learning and uncertain reasoning are crucial technologies in expert systems. An expert system model based on rough set theory is proposed. The model updates the existing decision rules and their parameters using an incremental methodology on the basis of the existing knowledge base and cases. In addition, the inference engine uses the rules and their parameters in the knowledge base to perform under uncertainty.