Computing Syntax Tree-based Minimal Unsatisfiable Cores of LTLf Formulas

Abstract Linear Temporal Logic on Finite Traces (LTLf) is a popular logic to express declarative specifications in Artificial Intelligence (AI). The recent call for explainable AI tools has made relevant the problem of computing efficiently minimal unsatisfiable cores (MUCs) and minimal correction sets (MCSes) of LTLf formulas. Recent work has focused on the extraction of MUCs on formulas in conjunctive form. In this paper, we present a method that operates on arbitrary formulas and computes a more refined notion of MUCs, as introduced by Schuppan, along with the corresponding notion of MCSes. Experiments show that our system, based on Answer Set Programming, outperforms available tools.