An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets

TL;DR

This paper introduces FastDiag, an efficient divide-and-conquer algorithm for diagnosing minimal faulty constraints in inconsistent constraint sets, improving performance over traditional conflict-based methods.

Contribution

The paper presents FastDiag, a novel divide-and-conquer diagnosis algorithm tailored for quick identification of minimal faulty constraints in over-constrained problems.

Findings

FastDiag outperforms conflict-directed hitting set methods in efficiency.

FastDiag effectively identifies minimal faulty constraints in various scenarios.

Performance analysis demonstrates the advantages of FastDiag over existing techniques.

Abstract

Constraint sets can become inconsistent in different contexts. For example, during a configuration session the set of customer requirements can become inconsistent with the configuration knowledge base. Another example is the engineering phase of a configuration knowledge base where the underlying constraints can become inconsistent with a set of test cases. In such situations we are in the need of techniques that support the identification of minimal sets of faulty constraints that have to be deleted in order to restore consistency. In this paper we introduce a divide-and-conquer based diagnosis algorithm (FastDiag) which identifies minimal sets of faulty constraints in an over-constrained problem. This algorithm is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial. We compare the performance of FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis that shows the advantages of our approach.