Comparing two types of knowledge-intensive CBR for optimized oil well drilling

This paper describes a new architecture for reasoning that combines case-based and model-based reasoning, referred to as knowledge intensive CBR (KiCBR). The case retrieval process is explained and compared through different reasoning approaches; plain CBR, and two forms of knowledgeintensive CBR. The mentioned methods are applied to problems in oil well drilling, a challenging domain. Knowledge-intensive methods in CBR will improve the case retrieval process. Our experiments show that one of the KiCBR methods, in which root causes of problems are included in the case description, has the highest accuracy compared to plain CBR and KiCBR without root causes included.