Use of fuzzy feature vectors and neural networks for case retrieval in case based systems

Case-based reasoning is a subset of artificial intelligence and expert systems, and is a powerful mechanism for developing systems that can learn from and adapt past experiences to solve current problems. One of the main tasks involved in the design of case-based systems is determining the features that make up a case and finding a way to index these cases in a case-base for efficient and correct retrieval. This paper looks at how the use of fuzzy feature vectors and neural networks can improve the indexing and retrieval steps in case-based systems.