Model-Based Diagnosis using Causal Networks

Adnan Darwiche

Abstract: This paper rests on several contributions. First, we introduce the notion of a consequence, which is a boolean expression that characterizes consistency-based diagnoses. Second, we introduce a basic algorithm for computing consequences when the system description is structured using a causal network. We show that if the causal network has no undirected cycles, then a consequence has a linear size and can be computed in linear time. Finally, we show that diagnoses characterized by a consequence and meeting some preference criterion can be extracted from the consequence in time linear in its size. A dual set of results is provided for abductive diagnosis.