Case-Based Reasoning means learning from previous experiences. Given the fact that this is a very general approach to human problem-solving behavior, it is more than natural that there are different approaches for implementing this process on computer systems. In commercial CBR systems, there are three main approaches that differ in the sources, materials, and knowledge they use. The textual CBR approach eases case acquisition. It is very useful in domains where large collections of know-how documents already exist and the intended user is able to immediately make use of the knowledge contained in the respective documents. The approach is well suited when there are not too many cases at a time (less than a couple of hundred) and when each case has a short description (three sentences at most). Otherwise, textual CBR retrieves a large number of cases that are irrelevant. The cost for controlling the quality of textual CBR is high. The conversational CBR approach is very useful for domains where a high volume of simple problems must be solved again and again. The system guides the agent and the customer with predefined dialogs. However, the case base is organized manually by the case author, which is a complex and costly activity when the cases are described by many attributes (questions). The conversational approach is well suited for applications in which only a few questions are needed for decision making. Maintenance costs are high because the developer must manually position each new case in a decision tree-like structure and update the ordering of the questions. The structural CBR approach relies on cases that are described with attributes and values that are pre-defined. In different structural CBR systems, attributes may be organized as flat tables, or as sets of tables with relations, or they may be structured in object-oriented manner. The structural CBR approach is useful in domains where additional knowledge, beside cases, must be used in order to produce good results. The domain model insures that new cases are of high quality and the maintenance effort is low. This approach always gives better results than the two others, but it requires an initial investment to produce the domain model.
