Adaptation through Planning in Knowledge Intensive CBR

Abstract

Adaptation is probably the most difficult task in Case-Based Reasoning (CBR) systems. Most techniques for adaptation propose ad-hoc solutions that require an effort on knowledge acquisition beyond typical CBR standards. In this paper we demonstrate the applicability of domain-independent planning techniques that exploit the knowledge already acquired in many knowledge-rich approaches to CBR. Those techniques are exemplified in a case-based training system that generates a 3D scenario from a declarative description of the training case.