Une étude des supports résiduels pour la consistance d'arc

Pour un algorithme établissant la consistance d'arc (AC), un support résiduel, ou résidu, est un support qui a été trouvé et enregistré lors d'une exécution de la procédure qui détermine si une valeur est supportée par une contrainte. Le point important est qu'un résidu n'offre pas la garantie de représenter un minorant du plus petit support courant de la valeur en question. Dans cet article, nous étudions l'impact théorique d'exploiter des résidus au niveau de l'algorithme élémentaire AC3. Tout d'abord, nous prouvons que AC3r(m) (i.e. AC3 exploitant des résidus) est optimal pour une dureté de contrainte faible ou élevée. Ensuite, nous montrons que MAC2001 présente, par rapport à MAC3r(m), un sur-coût en O(μed) par branche de l'arbre binaire construit par MAC, avec μ représentant le nombre de réfutations de la branche, e le nombre de contraintes et d la taille du plus grand domaine. L'une des conséquences est que, MAC3r(m) admet une complexité temporelle (dans le pire des cas) meilleure que MAC2001 pour une branche impliquant μ réfutations lorsque μ > d2 ou lorsque μ > d et que la dureté de chaque contrainte est soit faible soit élevée. Nos résultats expérimentaux montrent clairement que le fait d'exploiter des résidus permet d'améliorer l'efficacité des algorithmes MAC et SAC embarquant des algorithmes AC à gros grain

Maintaining Arc Consistency with Multiple Residues

International audienceExploiting residual supports (or residues) has proved to be one of the most cost-effective approaches for Maintaining Arc Consistency during search (MAC). While MAC based on optimal AC algorithm may have better theoretical time complexity in some cases, in practice the overhead for maintaining required data structure during search outweighs the benefit, not to mention themore complicated implementation. Implementing MAC with residues, on the other hand, is trivial. In this paper we extend previous work on residues and investigate the use of multiple residues during search. We first give a theoretical analysis of residue-based algorithms that explains their good practical performance. We then propose several heuristics on how to deal with multiple residues. Finally, our empirical study shows that with a proper and limited number of residues, many constraint checks can be saved. When the constraint check is expensive or a problem is hard, the multiple residues approach is competitive in both the number of constraint checks and cpu time

On the speed of constraint propagation and the time complexity of arc consistency testing

Establishing arc consistency on two relational structures is one of the most popular heuristics for the constraint satisfaction problem. We aim at determining the time complexity of arc consistency testing. The input structures $G$ and $H$ can be supposed to be connected colored graphs, as the general problem reduces to this particular case. We first observe the upper bound $O(e(G)v(H)+v(G)e(H))$, which implies the bound $O(e(G)e(H))$ in terms of the number of edges and the bound $O((v(G)+v(H))^3)$ in terms of the number of vertices. We then show that both bounds are tight up to a constant factor as long as an arc consistency algorithm is based on constraint propagation (like any algorithm currently known). Our argument for the lower bounds is based on examples of slow constraint propagation. We measure the speed of constraint propagation observed on a pair $G,H$ by the size of a proof, in a natural combinatorial proof system, that Spoiler wins the existential 2-pebble game on $G,H$. The proof size is bounded from below by the game length $D(G,H)$, and a crucial ingredient of our analysis is the existence of $G,H$ with $D(G,H)=\Omega(v(G)v(H))$. We find one such example among old benchmark instances for the arc consistency problem and also suggest a new, different construction.Comment: 19 pages, 5 figure

Inférence de supports pour les algorithmes de filtrage générique

http://www710.univ-lyon1.fr/~csolnonDans cet article, nous proposons une analyse statique des différentes contraintes d'un réseau afin d'identifier certaines propriétés (ou caractéristiques) générales. L'utilisation de ces propriétés rend possible une inférence de supports qui permet de réduire le nombre de tests de consistance. En effet, l'exploitation de certaines propriétés identifiées lors d'une phase de pré-traitement peut apporter une amélioration substantielle des algorithmes de recherche qui maintiennent une forme de consistance locale telle que la consistance d'arc. Les résultats d'expérimentations menées sur de nombreuses classes d'instances démontrent l'intérêt de cette approche

Metareasoning about propagators for constraint satisfaction

Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it is often very difficult to determine a priori which solving method is best suited to a problem. This work explores the use of machine learning to predict which solving method will be most effective for a given problem. We use four different problem sets to determine the CSP attributes that can be used to determine which solving method should be applied. After choosing an appropriate set of attributes, we determine how well j48 decision trees can predict which solving method to apply. Furthermore, we take a cost sensitive approach such that problem instances where there is a great difference in runtime between algorithms are emphasized. We also attempt to use information gained on one class of problems to inform decisions about a second class of problems. Finally, we show that the additional costs of deciding which method to apply are outweighed by the time savings compared to applying the same solving method to all problem instances

Consistency techniques in constraint networks

Ph.DDOCTOR OF PHILOSOPH

Efficient Calculation of Optimal Configuration Processes

Customers are getting increasingly involved in the design of the products and services they choose by specifying their desired characteristics. As a result, configuration systems have become essential technologies to support the development of mass-customization business models. These technologies facilitate the configuration of complex products and services that otherwise could generate many incorrect configurations and overwhelm users with confusion. This thesis studies the problem of optimizing the user interaction in a configuration process – as in minimizing the number of questions asked to a user in order to obtain a fully-specified product or service configuration. The work carried out builds upon a previously existing framework to optimize the process of configuring a software system, and focuses on improving its efficiency and generalizing its application to a wider range of configuration domains. Two solution methods along with two alternative ways of specifying the configuration models are proposed and studied on different configuration scenarios. The experimental study evidences that the introduced solutions overcome the limitations of the existing framework, resulting in more suitable algorithms to work with models involving a large number of configuration variables