Case-based Reasoning (CBR) is based on the intuition that new problems are often similar to previously encountered problems and, therefore, that past solutions may be of use in the current situation. Cases are often derived from legacy databases, thereby converting existing organisational resources into exploitable knowledge. CBR is particularly applicable to problems where the domain is not understood well enough for a robust statistical model or system of equations to be formulated. CBR is commonly used for diagnosis (or, more generally, for classification tasks), e.g., to determine a fault from observed attributes, or to determine whether or not cancer treatment is necessary given a set of past cases. AIAI has applied case-based reasoning to otherwise intractable problems such as fraud screening.
While the case-based reasoning methodology can be applied consistently across application domains, the implementation of the retrieval and similarity scoring functions is typically highly customised to the problem at hand. Two factors become critical: The availability of a flexible CBR Shell, and the accumulated practical experience of applying artificial intelligence techniques to real-world problems.