Learning Adaptation Rules from a Case-Base

摘要: A major challenge for case-based reasoning (CBR) is to overcome the knowledge-engineering problems incurred by developing adaptation knowledge. This paper describes an approach automating acquisition of knowledge overcoming many associated costs. makes use inductive techniques, which learn from case comparison. We also show how this can be usefully applied. The method has been tested in a property-evaluation CBR system and technique illustrated examples taken domain. In addition, we examine any available domain might exploited such adaptation-rule learning-system.