Constraint programming is a popular paradigm to deal with combinatorial problems in
artificial intelligence. Backtracking algorithms, applied to constraint networks, are commonly …

With the arrival of high throughput genotyping techniques, the detection of likely genotyping
errors is becoming an increasingly important problem. In this paper we are interested in …

Providing efficient black-box search procedures is one of the major concerns for constraint-
programming solvers. Most of the contributions in that area follow the fail-first principle …

Abstract Qualitative Spatial and Temporal Reasoning (QSR) is concerned with constraint-
based formalisms for representing, and reasoning with, spatial and temporal information …

The variable ordering heuristic is an important module in algorithms dedicated to solve
Constraint Satisfaction Problems (CSP), while it impacts the efficiency of exploring the …

The performance of a constraint model can often be improved by converting a subproblem
into a single table constraint. In this paper we study heuristics for identifying promising …

Constraint programming provides generic techniques to efficiently solve combinatorial
problems. In this paper, we tackle the natural question of using constraint solvers to sample …

Reinforcement learning has shown its relevance in designing search heuristics for
backtracking algorithms dedicated to solving decision problems under constraints. Recently …

As an extension of constraint networks, valued constraint networks (or valued CSPs) define
a unifying framework for modelling optimisation problems over finite domains in which the …

We consider the problem of selecting the best variable-value strategy for solving a given
problem in constraint programming. We show that the recent Embarrassingly Parallel …