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 …

Abstract We introduce GAC-4R, MDD-4, and MDD-4R three new algorithms for maintaining
arc consistency for table and MDD constraints. GAC-4R improves the well-known GAC-4 …

Table Constraints are very useful for modeling combinatorial problems in Constraint
Programming (CP). They are a universal mechanism for representing constraints, but …

In this document, we introduce XCSP3-core, a subset of XCSP3 that allows us to represent
constraint satisfaction/optimization problems. The interest of XCSP3-core is multiple:(i) …

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 …

Abstract Maintaining Generalized Arc Consistency (GAC) during search is considered an
efficient way to solve non-binary constraint satisfaction problems. Bit-based representations …

Constraint propagation is a key to the success of Constraint Programming (CP). The
principle is that filtering algorithms associated with constraints are executed in sequence …

Binary constraints are a general representation for constraints and is used in Constraint
Satisfaction Problems (CSPs). However, many problems are more easily modelled with non …

Table constraints define an arbitrary constraint explicitly as a set of solutions (tuples) or non-
solutions. Thus, space is proportional to number of tuples. Simple Tabular Reduction (STR) …

Many industrial applications require the use of table constraints (eg, in configuration
problems), sometimes of significant size. During the recent years, researchers have focused …