Cryptographic problems, operating within binary variable spaces, can be routinely
transformed into Boolean Satisfiability (SAT) problems regarding specific cryptographic …

Abstract Machine learning and logical reasoning have been the two foundational pillars of
Artificial Intelligence (AI) since its inception, and yet, until recently the interactions between …

Abstract We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning
(RL) algorithms for learning policies in tree MDPs, for which policy execution involves …

The Boolean satisfiability problem holds a significant place in computer science, finding
applications across various domains. This problem consists of looking for a truth assignment …

This paper improves SAT solvers by optimizing the initialization of truth values for variables
in a CNF formula. It introduces a Multilayer Perceptron (MLP) model that predicts optimal …

Cette thèse présente des contributions multiples et orthogonales à l'amélioration de la
résolution parallèle du problème de satisfiabilité booléenne (ou problème SAT). Une …

In this paper, we present a Bayesian Moment Matching (BMM) in-processing technique for
Conflict-Driven Clause-Learning (CDCL) SAT solvers. BMM is a probabilistic algorithm …

In this paper, we present a Bayesian Moment Matching (BMM) in-processing technique for
Conflict-Driven Clause-Learning (CDCL) SAT solvers. BMM is a probabilistic algorithm …

This document describes the SAT solver MapleSSV, as part of which we implemented a set
of heuristics that are found to be useful in solving SAT benchmarks that encode ARX …

Choosing the right approximation distribution family for variational inference (VI) requires
case-by-case design to strike a good balance between representation power and …