Model-Based Diagnosis using Structured System Descriptions

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.Comment: See http://www.jair.org/ for any accompanying file

A CPN-Approach for DistributedAbductive Reasoning : Application to Causal Model-Based Diagnosis

This thesis deals with fault diagnosis of distributed systems from a model-based view where Coloured Petri Nets are used to describe the systembehaviour. The systems concerned here are those comprising different interactingsubsystems. Coloured Behavioural Petri Nets are defined as a particular CPNintended for the description of a system’s causal behaviour, where each transitionis labelled with a matrix describing explicitly its firing ways. The use of suchmatrices helps in tackling the problem of complexity during backward analysis,and gives rise to a very specific technique based on reachability of CBPNs calledCW-analysis. CBPNs together with the CW-analysis are used to develop a dis-tributed model-based diagnosis approach. The diagnostic system is defined as setof diagnostic agents where each is assigned to diagnose a subsystem. Accordingly,the system model consists of a set of place-bordered CBPNs, whereas CW-analysisis exploited to implement a local diagnosis scheme. Once local diagnoses are ob-tained by the different agents, a cooperation process should be initiated to ensureglobal consistency of such diagnoses

Preferring Diagnoses by Abduction

International audienc