Selecting Most Adaptable Diagnostic Solutions through Pivoting-Based Retrieval

The aim of the present paper is to investigate a retrieval strategy for case-based diagnosis called Pivoting Based Retrieval (PBR), based on a tight integration between retrieval and adaptation estimation. It exploits a heuristic estimate of the adaptability of a solution; during retrieval, lower and upper bounds for such an estimate are computed for relevant cases and a pivot case is selected, determining which cases have to be considered and which have not. Such a technique has been evaluated on three different domain models and very satisfactory results have been obtained both in terms of accuracy, space and retrieval time