Towards Dynamic Maintenance of Retrieval Knowledge in CBR

May 2002

Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2002)

Volume

Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2002)

All Papers

The utility problem is observable in many learning systems including case-based reasoning (CBR). In-dexing strategies have been implemented in CBR to overcome the effects of the utility problem but have been criticised as although improving retrieval effi-ciency they can reduce the competency of solutions and can be difficult to maintain. Here we present a novel indexing strategy based on a modified k-means clustering algorithm. We demonstrate that such an indexing strategy improves retrieval efficiency with-out adversely affecting solution competency. Impor-tantly it also provides a means for the dynamic real time maintenance of retrieval knowledge thus ensur-ing that the index is always optimal.