Constraint Programming (CP) is a useful technology for modeling and solving combinatorial
constrained problems. On the one hand, on can use a library like PyCSP3 for easily …

We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated
representations of combinatorial constrained problems. This new format is able to deal with …

In this document, we introduce PyCSP $3 $, a Python library that allows us to write models of
combinatorial constrained problems in a declarative manner. Currently, with PyCSP $3 …

Ad-hoc constraints (also called generic constraints) are important for modelling Constraint
Satisfaction Problems (CSPs). Many representations have been proposed to define ad-hoc …

Proof logging provides an auditable way of guaranteeing that a solver has produced a
correct answer using sound reasoning. This is standard practice for Boolean satisfiability …

Constraint Programming is a powerful paradigm to model and solve combinatorial problems.
While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps …

Table constraints are very useful for modeling combinatorial constrained problems, and thus
play an important role in Constraint Programming (CP). During the last decade, many …

Table constraints are instrumental in modelling combinatorial problems with Constraint
Programming. Recently, Compact-Table (CT) has been proposed and shown to be as an …

Recently, the Binary Constraint Tree (BCT), a tree structured Binary Constraint Network
(BCN), has been shown to be more succinct than various ad-hoc constraints. In this paper …

These last years, there has been a growing interest for structures like tables and decision
diagrams in Constraint Programming (CP). This is due to the universal character of these …