Symmetry Breaking Constraints: Recent Results

Symmetry is an important problem in many combinatorial problems. One way of dealing with symmetry is to add constraints that eliminate symmetric solutions. We survey recent results in this area, focusing especially on two common and useful cases: symmetry breaking constraints for row and column symmetry, and symmetry breaking constraints for eliminating value symmetryComment: To appear in Proceedings of Twenty-Sixth Conference on Artificial Intelligence (AAAI-12

Génération rapide et filtrage de configurations canoniques

La configuration sous contraintes présente une nouvelle difficulté à prendre en compte par les méthodes d'élimination de symétries connues par la communauté CSP car elle y introduit un aspect dynamique. Nous présentons ici une amélioration significative d'un algorithme de génération de configurations canoniques. Cette nouvelle version exploite l'incrémentalité que l'on peut faire ressortir de la génération de solutions canoniques et de l'ordre total sur les arbres sur laquelle elle repose. La complexité du test de canonicité passe ainsi de O(Nlog(N)) à O(N). De plus, une technique de filtrage nous permet d'éliminer à l'avance des configurations non canoniques. Des résultats expérimentaux montrent l'intérêt de cette approche sur des problèmes classiques

Elimination des symétries locales durant la résolution dans les CSPs

Plusieurs approches exploitant l'élimination des symétries dans la résolution des CSPs sont apparues récemment. La grande majorité de ces méthodes exploitent les symétries globales du problème étudié et ne tente pas d'exploiter les symétries locales. Il a été montré que l'élimination des symétries globales peut être utile dans la résolution des CSPs. Mais exploiter uniquement ces symétries peut ne pas suffire pour résoudre des problèmes difficiles contenant de nombreuses symétries locales. En effet, un problème peut avoir peu ou pas du tout de symétries initiales (globales) et devenir très symétrique à certains noeuds durant la recherche. Dans ce papier, nous étudions le principe général de la symétrie sémantique et on définit la symétrie syntaxique qui est une condition suffisante de la symétrie sémantique. Nous montrons comment la symétrie syntaxique est détectée et éliminée localement pour améliorer l'efficacité des méthodes de résolution de CSPs. Les expérimentations confirment que l'exploitation des symétries locales est profitable dans la résolution des CSPs

Partial Symmetry Breaking by Local Search in the Group

The presence of symmetry in constraint satisfaction problems can cause a great deal of wasted search effort, and several methods for breaking symmetries have been reported. In this paper we describe a new method called Symmetry Breaking by Nonstationary Optimisation, which interleaves local search in the symmetry group with backtrack search on the constraint problem. It can be tuned to break each symmetry with an arbitrarily high probability with high runtime overhead, or as a lightweight but still powerful method with low runtime overhead. It has negligible memory requirement, it combines well with static lex-leader constraints, and its benefit increases with problem hardness

Modern techniques for constraint solving the CASPER experience

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaConstraint programming is a well known paradigm for addressing combinatorial problems which has enjoyed considerable success for solving many relevant industrial and academic problems. At the heart of constraint programming lies the constraint solver, a computer program which attempts to find a solution to the problem, i.e. an assignment of all the variables in the problemsuch that all the constraints are satisfied. This dissertation describes a set of techniques to be used in the implementation of a constraint solver. These techniques aim at making a constraint solver more extensible and efficient,two properties which are hard to integrate in general, and in particular within a constraint solver. Specifically, this dissertation addresses two major problems: generic incremental propagation and propagation of arbitrary decomposable constraints. For both problemswe present a set of techniques which are novel, correct, and directly concerned with extensibility and efficiency. All the material in this dissertation emerged from our work in designing and implementing a generic constraint solver. The CASPER (Constraint Solving Platformfor Engineering and Research)solver does not only act as a proof-of-concept for the presented techniques, but also served as the common test platform for the many discussed theoretical models. Besides the work related to the design and implementation of a constraint solver, this dissertation also presents the first successful application of the resulting platform for addressing an open research problem, namely finding good heuristics for efficiently directing search towards a solution

On the relationship between satisfiability and partially observable Markov decision processes

Stochastic satisfiability (SSAT), Quantified Boolean Satisfiability (QBF) and decision-theoretic planning in finite horizon partially observable Markov decision processes (POMDPs) are all PSPACE-Complete problems. Since they are all complete for the same complexity class, I show how to convert them into one another in polynomial time and space. I discuss various properties of each encoding and how they get translated into equivalent constructs in the other encodings. An important lesson of these reductions is that the states in SSAT and flat POMDPs do not match. Therefore, comparing the scalability of satisfiability and flat POMDP solvers based on the size of the state spaces they can tackle is misleading. A new SSAT solver called SSAT-Prime is proposed and implemented. It includes improvements to watch literals, component caching and detecting symmetries with upper and lower bounds under certain conditions. SSAT-Prime is compared against a state of the art solver for probabilistic inference and a native POMDP solver on challenging benchmarks