Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems

In this paper, we present an approach that integrates symbolic rules, neural networks and cases. To achieve it, we integrate a kind of hybrid rules, called neurules, with cases. Neurules integrate symbolic rules with the Adaline neural unit. In the integration, neurules are used to index cases representing their exceptions. In this way, the accuracy of the neurules is improved. On the other hand, due to neurule-based efficient inference mechanism, conclusions can be reached more efficiently. In addition, neurule-based inferences can be performed even if some of the inputs are unknown, in contrast to symbolic rule-based inferences. Furthermore, an existing symbolic rule-base with indexed exception cases can be converted into a neurule-base with corresponding indexed exception cases. Finally, empirical data can be used as a knowledge source, which facilitates knowledge acquisition. We also present a new high-level categorization of the approaches integrating rule-based and case-based reasoning.