The knowledge stored in a case-base is central to the problem-solving of a Case-Based Reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem-solving. For many CBR applications the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This paper focuses on the optimisation of the parameters and feature selections/weights for the indexing and nearest-neighbour algorithms used by CBR retrieval. Optimisation is applied after case-base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case-base. The optimisation process is generic and automatic, using knowledge contained in the cases. In this paper we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case-base, is provided by two snap-shots of a formulation database. A change in the company's formulation policy results in a second, more fundamental, requirement for CBR maintenance. We show that, after case-base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimise the retrieval process.
Our earlier work acquiring similarity knowledge for the retrieve stage has also used introspective learning to exploit the knowledge already in the case-base REF .
Maintaining Retrieval Knowledge in a Case-Based Reasoning System
Comput. Intell.
Comput. Intell.
Computer Science