Local Diagnosis

TL;DR

This paper introduces a method for model-based diagnosis that isolates relevant system description parts for efficient diagnosis, building on previous belief change operations that target relevant belief base segments.

Contribution

It applies belief change operations to model-based diagnosis by isolating relevant system description parts for more efficient problem solving.

Findings

Effective isolation of relevant system description improves diagnosis efficiency

Method reduces complexity by focusing on relevant system components

Demonstrates applicability to model-based diagnosis problems

Abstract

In an earlier work, we have presented operations of belief change which only affect the relevant part of a belief base. In this paper, we propose the application of the same strategy to the problem of model-based diangosis. We first isolate the subset of the system description which is relevant for a given observation and then solve the diagnosis problem for this subset.