Arrow Research search
Back to ECAI

ECAI 2023

MaxSAT-Based Inconsistency Measurement

Conference Paper Accepted Paper Artificial Intelligence

Abstract

Inconsistency measurement aims at obtaining a quantitative assessment of the level of inconsistency in knowledge bases. While having such a quantitative assessment is beneficial in various settings, inconsistency measurement of propositional knowledge bases is under most existing measures a significantly challenging computational task. In this work, we harness Boolean satisfiability (SAT) based solving techniques for developing practical inconsistency measurement algorithms. Our algorithms—some of which constitute, to the best of our knowledge, the first practical approaches for specific inconsistency measures—are based on using natural choices of SAT-based techniques for the individual inconsistency measures, ranging from direct maximum satisfiability (MaxSAT) encodings to MaxSAT-based column generation techniques making use of incremental computations. We show through an extensive empirical evaluation that our approaches scale well in practice and significantly outperform recently-proposed answer set programming approaches to inconsistency measurement.

Authors

Keywords

No keywords are indexed for this paper.

Context

Venue
European Conference on Artificial Intelligence
Archive span
1982-2025
Indexed papers
5223
Paper id
451982812508588344
v2026.09.13