Model-Based Diagnosis using Structured System Descriptions

TL;DR

This paper develops a structured, graph-based approach for model-based diagnosis, introducing new syntactic characterizations and algorithms for efficiently computing and enumerating preferred diagnoses in systems with or without cycles.

Contribution

It introduces a consequence-based framework with NNF variations and algorithms for diagnosis computation, linking complexity to system structure topology.

Findings

Linear-size consequences in acyclic systems can be computed in linear time.

The complexity of consequence computation depends on system structure topology.

An efficient algorithm for enumerating preferred diagnoses is proposed.

Abstract

This paper presents a comprehensive approach for model-based diagnosis which includes proposals for characterizing and computing preferred diagnoses, assuming that the system description is augmented with a system structure (a directed graph explicating the interconnections between system components). Specifically, we first introduce the notion of a consequence, which is a syntactically unconstrained propositional sentence that characterizes all consistency-based diagnoses and show that standard characterizations of diagnoses, such as minimal conflicts, correspond to syntactic variations on a consequence. Second, we propose a new syntactic variation on the consequence known as negation normal form (NNF) and discuss its merits compared to standard variations. Third, we introduce a basic algorithm for computing consequences in NNF given a structured system description. We show that if the system structure does not contain cycles, then there is always a linear-size consequence in NNF which can be computed in linear time. For arbitrary system structures, we show a precise connection between the complexity of computing consequences and the topology of the underlying system structure. Finally, we present an algorithm that enumerates the preferred diagnoses characterized by a consequence. The algorithm is shown to take linear time in the size of the consequence if the preference criterion satisfies some general conditions.