Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

One of the most important paradigm shifts in the use of SAT solvers for solving industrial
problems has been the introduction of clause learning. Clause learning entails adding a …

Abstract Explanations of Machine Learning (ML) models often address a question. Such
explanations can be related with selecting feature-value pairs which are sufficient for the …

Decision lists (DLs) find a wide range of uses for classification problems in Machine
Learning (ML), being implemented in anumber of ML frameworks. DLs are often perceived …

Widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models
on the one hand and a number of crucial issues pertaining to them warrant the need for …

The analysis of inconsistent formulas finds an everincreasing range of applications, that
include axiom pinpointing in description logics, fault localization in software, model-based …

Boolean Satisfiability (SAT) is arguably the archetypical NP-complete decision problem.
Progress in SAT solving algorithms has motivated an ever increasing number of practical …

The enumeration of all Maximal Satisfiable Subsets (MSSes) or all Minimal Correction
Subsets (MCSes) of an unsatisfiable CNF Boolean formula is a useful and sometimes …

This paper considers unsatisfiable CNF formulas and addresses the problem of computing
the union of the clauses included in some minimally unsatisfiable subformula (MUS). The …