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 …

Recent work proposed a toolkit PySAT aiming at fast and easy prototyping with propositional
satisfiability (SAT) oracles in Python, which enabled one to exploit the power of the original …

Tree ensembles (TEs) denote a prevalent machine learning model that do not offer
guarantees of interpretability, that represent a challenge from the perspective of explainable …

Despite their impressive capabilities, large pre-trained language models (LMs) struggle with
consistent reasoning; recently, prompting LMs to generate explanations that self-guide the …

ASP solvers address several reasoning tasks that go beyond the mere computation of
answer sets. Among them are cautious reasoning, for modeling query entailment, and …

In the last couple of decades, developments in SAT-based optimization have led to highly
efficient maximum satisfiability (MaxSAT) solvers, but in contrast to the SAT solvers on which …

Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …

The problem of scheduling chemotherapy treatments in oncology clinics is a complex
problem, given that the solution has to satisfy (as much as possible) several requirements …

Abstract Maximum Satisfiability (MaxSAT), the optimisation extension of the well-known
Boolean Satisfiability (SAT) problem, is a competitive approach for solving NP-hard …

We study a class of reinforcement learning tasks in which the agent receives its reward for
complex, temporally-extended behaviors sparsely. For such tasks, the problem is how to …