Model-Based Diagnosis with the Default-Based Diagnosis Engine: Effective Control Strategies that Work in Practice

This paper presents a set of focusing methods for model-based diagnosis systems. They aim at restricting the efforts spent on different computational tasks: the generation of diagnosis candidates, model-based prediction, and dependency recording. Building upon our previous work on exploiting a preference order on component faults in the default-based diagnosis engine (DDE), we formally describe the focusing principles and show their validity in a default logic framework.