Inference and reasoning in a Bayesian knowledge-intensive CBR system

Abstract This paper presents the inference and reasoning methods in a Bayesian supported knowledge-intensive case-based (CBR) system called BNCreek. The process this is combination of three methods. semantic network CBR method are employed to handle difficulties inferencing uncertain domains. make more accurate. An experiment from oil well drilling as complex application domain conducted. evaluated against expert estimations compared with seven other corresponding systems. normalized discounted cumulative gain (NDCG) rank-based metric, weighted error (WE), root-square (RSE) statistical metrics evaluate different aspects capabilities. results show efficiency developed