Abstract
Case-based reasoning is a popular approach used in intelligent systems. It is particularly useful in domains where an abundant number of past cases is available. Cases encompass knowledge accumulated from specific (specialized) situations. Whenever a new case has to be dealt with, the most similar cases are retrieved from the case base and their encompassed knowledge is exploited in the current situation. Combinations of case-based reasoning with other intelligent methods have been explored deriving effective knowledge representation schemes. Although some types of combinations have been mostly explored, other types have not been thoroughly investigated. In this paper, we briefly outline popular case-based reasoning combinations. More specifically, we focus on combinations of case-based reasoning with rule-based reasoning, soft computing methods (i.e., fuzzy methods, neural networks, genetic algorithms) and ontologies. We illustrate basic types of such combinations and also point out future directions.