Model enumeration in propositional circumscription via unsatisfiable core analysis

TL;DR

This paper introduces a novel algorithm utilizing unsatisfiable core analysis for enumerating minimal models in propositional circumscription, demonstrating improved efficiency over existing solvers on practical problems.

Contribution

The paper presents a new algorithm for minimal model enumeration in propositional circumscription using unsatisfiable core analysis, along with a competitive solver implementation.

Findings

The new solver outperforms existing tools on practical benchmarks.

Empirical results show significant efficiency improvements.

The approach effectively handles real-world problems involving minimal models.

Abstract

Many practical problems are characterized by a preference relation over admissible solutions, where preferred solutions are minimal in some sense. For example, a preferred diagnosis usually comprises a minimal set of reasons that is sufficient to cause the observed anomaly. Alternatively, a minimal correction subset comprises a minimal set of reasons whose deletion is sufficient to eliminate the observed anomaly. Circumscription formalizes such preference relations by associating propositional theories with minimal models. The resulting enumeration problem is addressed here by means of a new algorithm taking advantage of unsatisfiable core analysis. Empirical evidence of the efficiency of the algorithm is given by comparing the performance of the resulting solver, CIRCUMSCRIPTINO, with HCLASP, CAMUS MCS, LBX and MCSLS on the enumeration of minimal models for problems originating from practical applications. This paper is under consideration for acceptance in TPLP.