45 research outputs found
Toward an automaton Constraint for Local Search
We explore the idea of using finite automata to implement new constraints for
local search (this is already a successful technique in constraint-based global
search). We show how it is possible to maintain incrementally the violations of
a constraint and its decision variables from an automaton that describes a
ground checker for that constraint. We establish the practicality of our
approach idea on real-life personnel rostering problems, and show that it is
competitive with the approach of [Pralong, 2007]
On Global Warming (Softening Global Constraints)
We describe soft versions of the global cardinality constraint and the
regular constraint, with efficient filtering algorithms maintaining domain
consistency. For both constraints, the softening is achieved by augmenting the
underlying graph. The softened constraints can be used to extend the
meta-constraint framework for over-constrained problems proposed by Petit,
Regin and Bessiere.Comment: 15 pages, 7 figures. Accepted at the 6th International Workshop on
Preferences and Soft Constraint
On Matrices, Automata, and Double Counting
Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables M, with the same constraint defined by a finite-state automaton A on each row of M and a global cardinality constraint gcc on each column of M. We give two methods for deriving, by double counting,
necessary conditions on the cardinality variables of the gcc constraints from the automaton A. The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We evaluate the impact of our methods on a large set of nurse rostering problem instances
Prefix-Projection Global Constraint for Sequential Pattern Mining
Sequential pattern mining under constraints is a challenging data mining
task. Many efficient ad hoc methods have been developed for mining sequential
patterns, but they are all suffering from a lack of genericity. Recent works
have investigated Constraint Programming (CP) methods, but they are not still
effective because of their encoding. In this paper, we propose a global
constraint based on the projected databases principle which remedies to this
drawback. Experiments show that our approach clearly outperforms CP approaches
and competes well with ad hoc methods on large datasets
On the Reification of Global Constraints
We introduce a simple idea for deriving reified global constraints in a systematic way. It is based on
the observation that most global constraints can be reformulated as a conjunction of pure functional dependency
constraints together with a constraint that can be easily reified. We first show how the core constraints of the
Global Constraint Catalogue can be reified and we then identify several reification categories that apply to at
least 82% of the constraints in the Global Constraint Catalogue
Relaxation lagrangienne pour le filtrage d'une contrainte-automate à coûts multiples
Relaxation lagrangienne pour le filtrage d'une contrainte-automate à coûts multiple