A case retrieval model based on factor-structure connection and λ - Similarity in fuzzy Case-based reasoning

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Abstract

One of the fundamental goals of artificial intelligence (AI) is to build artificially computer-based systems which make computer simulate, extend and expand human's intelligence and empower computers to perform tasks which are routinely performed by human beings. Without effective reasoning mechanism, it is impossible to make computer think and reason. Research on reasoning is an interesting and meaningful problem. Case-based reasoning is a branch of reasoning research. There are a lot of research results and successful application on Case-based reasoning. How to index and retrieve similar cases is one of key issue in Case-based reasoning research hotspots in CBR research works. In addition, there is a lot of uncertainty information in daily work and everyday life. So how to deal with uncertainty information in Case-based Reasoning effectively becomes more and more important. In this paper, a new case indexing and retrieval model which can deal with both fuzzy information and accurate information is presented. © Springer-Verlag Berlin Heidelberg 2005.

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Meng, D., Zhang, Z., & Xu, Y. (2005). A case retrieval model based on factor-structure connection and λ - Similarity in fuzzy Case-based reasoning. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3613, pp. 175–178). Springer Verlag. https://doi.org/10.1007/11539506_22

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