Adaptation Rule Learning for Case-Based Reasoning

摘要：

A method of learning adaptation rules for casebased reasoning (CBR) is proposed in this paper. Adaptation rules are generated from the case-base with the guidance of domain knowledge which is also extracted from the case-base. The adaptation rules are refined before they are applied in the revision process. After solving each new problem, the adaptation rule set is updated by an evolution module in the retention process. The results of preliminary experiment show that the adaptation rules obtained could improve the performance of the CBR system compared to a retrieval-only CBR system.

作者: Huan Li Dawei Hu Tianyong Hao Liu Wenyin Xiaoping Chen

作者单位: Department of Computer Science and Technology, University of Science & Technology of China, Hefei, C Department of Computer Science and Technology, University of Science & Technology of China, Hefei, C Department of Computer Science, City University of Hong Kong, Hong Kong, China Joint Research Lab of Excellence, CityU-USTC Advanced Research Institute, Suzhou, China; Department Department of Computer Science and Technology, University of Science & Technology of China, Hefei, C

会议类型: 国际会议

会议名称: 2007年第三届语义和知识网格国际会议(Third International Conference on Semantics,Knowledge,and Grid)(SKG 2007)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-10-29（万方平台首次上网日期，不代表论文的发表时间）