Ming Adaptation Rules from Cases in CBR Systems

摘要

Case based reasoning (CBR) is a well known framework to cope with ill-structured tasks, where no enough domain knowledge is available [2]. The main objective of CBR is to address the knowledge acquisition bottleneck. Namely, in CBR the reasoner does not make effort to build an abstract model for domain knowledge to solve the problem, instead, during the problem solving, it relies on the past similar cases, and attempts to find the appropriate solution for the problem at hand, by modifying the past similar solutions. However, CBR systems also require substantial knowledge acquisition effort (e.g. acquiring cases, case vocabulary, retrieval knowledge, adaptation knowledge [5]). This knowledge traditionally is derived from a domain expert. Accordingly, although the expert can not propose an abstract model to support the domain, s/he attempts to define some regulartities in the domain, that makes it possible to reason with the cases.