Categorizing approaches combining rule-based and case-based reasoning

Rule-based and case-based reasoning are two popular approaches used in intelligent systems. Rules usually represent general knowledge, whereas cases encompass knowledge accumulated from specific (specialized) situations. Each approach has advantages and disadvantages, which are proved to be complementary in a large degree. So, it is well-justified to combine rules and cases to produce effective hybrid approaches, surpassing the disadvantages of each component method. In this paper, we first present advantages and disadvantages of rule-based and case-based reasoning and show that they are complementary. We then discuss the deficiencies of existing categorization schemes for integrations of rule-based and case-based representations. To deal with those deficiencies, we introduce a new categorization scheme. Finally, we briefly present representative approaches for the final categories of our scheme.