A parallelized indexing method for large-scale case-based reasoning

Case-based reasoning (CBR) is a commonly seen problem-solving methodology in artificial intelligence. It can correctly take advantage of the situations and methods in former cases to find out suitable solutions for new problems. CBR must accurately retrieve similar prior cases for getting a good performance. In the past, many researchers proposed useful technologies to handle this problem. However, the performance of retrieving similar cases may be greatly influenced by the number of cases. In this paper, the performance issue of large-scale CBR is discussed and a parallelized indexing architecture is then proposed for efficiently retrieving similar cases in large-scale CBR. Several algorithms for implementing the proposed architecture are also described. Some experiments are made and the results show the efficiency of proposed method.

• Type: Journal
• Year: 2002
• Where: ESWA
• Authors: Wei-Chou Chen, Shian-Shyong Tseng, Lu-Ping Chang, Tzung-Pei Hong, Mon-Fong Jiang
• Keywords: ESWA 2002, Large-scale Cbr, Similar Cases, Similar Prior Cases