Learning and Applying Case-Based Adaptation Knowledge

Abstract

Adaptation is an important step in CBR when applied to design tasks. However adaptation knowledge can be difficult to acquire directly from an expert. Nevertheless, CBR tools provide few facilities to assist with the acquisition of adaptation knowledge. This paper considers a special class of design task, where a component-based solution can be developed in stages, and suggests adaptation knowledge that is suited to CBR systems for component-based design. A case-based adaptation is proposed where the adaptation cases are generated from the original problem-solving case-base, and so knowledge acquisition is automated. Both numeric and nominal targets are adapted, although a different case-based adaptation is applied for each. The gains of adaptation are presented for a tablet formulation application, although the approach is suited for other formulation and configuration tasks that apply a component-based approach to design. The learned adaptation knowledge is understandable to the expert, with the effect that he can criticise the content and refine the knowledge if necessary. Results are promising but the case-based adaptation systems offer many opportunities for optimisation and further learning.