21 research outputs found
Hybrid tractability of soft constraint problems
The constraint satisfaction problem (CSP) is a central generic problem in
computer science and artificial intelligence: it provides a common framework
for many theoretical problems as well as for many real-life applications. Soft
constraint problems are a generalisation of the CSP which allow the user to
model optimisation problems. Considerable effort has been made in identifying
properties which ensure tractability in such problems. In this work, we
initiate the study of hybrid tractability of soft constraint problems; that is,
properties which guarantee tractability of the given soft constraint problem,
but which do not depend only on the underlying structure of the instance (such
as being tree-structured) or only on the types of soft constraints in the
instance (such as submodularity). We present several novel hybrid classes of
soft constraint problems, which include a machine scheduling problem,
constraint problems of arbitrary arities with no overlapping nogoods, and the
SoftAllDiff constraint with arbitrary unary soft constraints. An important tool
in our investigation will be the notion of forbidden substructures.Comment: A full version of a CP'10 paper, 26 page
Variable and value elimination in binary constraint satisfaction via forbidden patterns
Variable or value elimination in a constraint satisfaction problem (CSP) can
be used in preprocessing or during search to reduce search space size. A
variable elimination rule (value elimination rule) allows the polynomial-time
identification of certain variables (domain elements) whose elimination,
without the introduction of extra compensatory constraints, does not affect the
satisfiability of an instance. We show that there are essentially just four
variable elimination rules and three value elimination rules defined by
forbidding generic sub-instances, known as irreducible existential patterns, in
arc-consistent CSP instances. One of the variable elimination rules is the
already-known Broken Triangle Property, whereas the other three are novel. The
three value elimination rules can all be seen as strict generalisations of
neighbourhood substitution.Comment: A full version of an IJCAI'13 paper to appear in Journal of Computer
and System Sciences (JCSS
Forbidden patterns in constraint satisfaction problems
Le problème de satisfaction de contraintes (CSP) est NP-complet, même dans le cas où toutes les contraintes sont binaires. Cependant, certaines classes d'instances CSP sont traitables. Récemment, une nouvelle méthode pour définir de telles classes aémergée. Cette approche est centrée autour des motifs interdits, ou l'absence locale de certaines conditions. Elle est l'objet de ma thèse. Nous définissons formellement ce que sont les motifs interdits, présentons les propriétés qu'ils détiennent, et finalement les utilisons afin d'établir plusieurs résultats de complexité importants. En utilisant différentes versions de motifs, toutes basées sur le même concept de base, nous énumérons un nombre important de nouvelles classes traitables, ainsi que certaines NP-completes. Nous combinons ces résultats pour révéler plusieurs dichotomies, chacune englobant une large gamme de classes d'instances CSP. Nous montrons aussi que les motifs interdits représentent un outil intéressant pour la simplification d'instances CSPs. Nous donnons plusieurs nouveaux moyens de réduire la taille des
instances CSP, que ce soit en éliminant des variables ou en fusionnant les domaines, et montrons comment ces méthodes sont activées par l'absence locale de certains modèles. Comme les conditions de leurutilisation sont entièrement locales, nos opérations peuvent être utilisés sur un large éventail de problèmes.The Constraint Satisfaction Problem (CSP) is NP-Complete, even in the case where all constraints are binary. However, some classes of CSP instances are tractable. Recently, a new method for defining such classes has emerged. This approach is centered around forbidden patterns, or the local absence of some conditions. It is the focus of my thesis. We formally define what forbidden patterns are, exhibit the properties they hold, and eventually put them to use in order to establish several important tractability results. Using different versions of patterns, all based on the same core concept, we list a significant number of new tractable classes, as well as some NP-Complete ones. We combine these results to reveal several dichotomies, each one encompassing a large range of classes of CSP instances. We also show how useful a tool forbidden patterns can be in the field of CSP instance simplification. We give multiple new ways of decreasing the size of CSP instances, whether by eliminating variables or fusioning domains, and prove how all these methods are enabled by the local absence of some patterns. Since the conditions for their use are entirely local, our operations can be used on a wide array of problems
Tractability in Constraint Satisfaction Problems: A Survey
International audienceEven though the Constraint Satisfaction Problem (CSP) is NP-complete, many tractable classes of CSP instances have been identified. After discussing different forms and uses of tractability, we describe some landmark tractable classes and survey recent theoretical results. Although we concentrate on the classical CSP, we also cover its important extensions to infinite domains and optimisation, as well as #CSP and QCSP
Graph Structures for Knowledge Representation and Reasoning
This open access book constitutes the thoroughly refereed post-conference proceedings of the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence. The 7 revised full papers presented together with 2 invited contributions were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which allows to bridge the gap between the different communities
Investigation of Matching Problems using Constraint Programming and Optimisation Methods
This thesis focuses on matching under ordinal preferences, i.e. problems where agents may be required to list other agents that they find acceptable in order of preference. In particular, we focus on two main cases: the popular matching and the kidney exchange problem. These problems are important in practice and in this thesis we develop novel algorithms and techniques to solve them as combinatorial optimisation problems. The first part of the thesis focuses on one-sided matching on a bipartite graph, specifically the popular matching. When the participants express their preferences in an ordinal order, one might want to guarantee that no two applicants are inclined to form a coalition in order to maximise their welfare, thus finding a stable matching is needed. Popularity is a concept that offers an attractive trade- off between these two notions. In particular, we examine the popular matching in the context of constraint programming using global constraints. We discuss the possibility to find a popular matching even for the instances that does not admit one.
The second part of the thesis focuses on non-bipartite graphs, i.e. the kidney exchange problem. Kidney transplant is the most effective treatment to cure end-stage renal disease, affecting one in every thousand European citizen. Motivated by the observation that the kidney exchange is inherently a stochastic online problem, first, we give a stochastic online method, which provides an expected value estimation that is correct within the limit of sampling errors. Second, we show that by taking into consideration a probabilistic model of future arrivals and drop-offs, we can get reduce sampling scenarios, and we can even construct a sampling-free probabilistic model, called the Abstract Exchange Graph (AEG). A final contribution of this thesis is related to finding robust solutions when uncertainty occurs. Uncertainty is inherent to most real world problems