Error repair and knowledge acquisition via case-based reasoning

Abstract

To cope with the knowledge acquisition bottleneck, the authors propose a new architecture combining rule-based reasoning (RBR), case-based reasoning (CBR) and knowledge acquisition technology in a system which solves pattern search problems. The RBR part searches for specified patterns in a large space represented by a network structure such as an LSI circuit diagram, which contains a great number of patterns and variations. It then carries out specified actions, such as fault diagnosis, on the patterns that are found. The outputs of the RBR part are transferred to the CBR part. The user of the system detects and repairs a few pattern detection errors caused by the RBR part. The CBR part detects and repairs all remaining errors which can be estimated from the user detected ones. The repaired results are sent back to the RBR part to recover the RBR output. The repaired results are also stored automatically in the case base. Similar cases are grouped in a same case family. The knowledge acquisition part relates each case family to an incomplete rule in the RBR knowledge base and proposes modifying the rule. Eventually, the system can obtain refined rules with the cooperation of domain experts. Thus, the problem solving process and knowledge acquisition process are performed cyclically. The architecture was successfully applied to a pair condition extraction problem for an analog LSI circuit layout system.