| Summary: | 碩士 === 國立交通大學 === 資訊科學系 === 88 === Case-based reasoning (CBR) is a methodology of problem-solving in artificial intelligence. Just like human being, CBR uses prior cases to find out suitable solution for the new problems. Unlike the others, CBR pays attention to the characteristics of each case. CBR can correctly take advantage of the situations and methods in former cases to solve problems. A critical task of CBR is to retrieve similar prior cases accurately and many researchers have proposed some useful technologies to handle such problem. However, increasingly larger number of cases influences the performance of retrieving similar cases for the large-scale CBR was seldom been discussed. In this thesis, the performance issue of large-scale CBR is discussed and a new indexing method, called bit-wise indexing method, and the corresponding efficient algorithms are proposed for retrieving the similar cases in large-scale CBR efficiently. The bit-wise indexing method and the corresponding algorithm can be easily parallelized and thus gets great performance improvement in case retrieving and similarity measuring. Some experiments are made for comparing the performance with other methods and the results show the performance of proposed method is admirable.
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