Harnessing tractability in constraint satisfaction problems

The Constraint Satisfaction Problem (CSP) is a fundamental NP-complete problem with many applications in artificial intelligence. This problem has enjoyed considerable scientific attention in the past decades due to its practical usefulness and the deep theoretical questions it relates to. However, there is a wide gap between practitioners, who develop solving techniques that are efficient for industrial instances but exponential in the worst case, and theorists who design sophisticated polynomial-time algorithms for restrictions of CSP defined by certain algebraic properties. In this thesis we attempt to bridge this gap by providing polynomial-time algorithms to test for membership in a selection of major tractable classes. Even if the instance does not belong to one of these classes, we investigate the possibility of decomposing efficiently a CSP instance into tractable subproblems through the lens of parameterized complexity. Finally, we propose a general framework to adapt the concept of kernelization, central to parameterized complexity but hitherto rarely used in practice, to the context of constraint reasoning. Preliminary experiments on this last contribution show promising results

Point-width and Max-CSPs

International audienceThe complexity of (unbounded-arity) Max-CSPs under structural restrictions is poorly understood. The two most general hypergraph properties known to ensure tractability of Max-CSPs, β-acyclicity and bounded (incidence) MIM-width, are incomparable and lead to very different algorithms. We introduce the framework of point decompositions for hypergraphs and use it to derive a new sufficient condition for the tractability of (structurally restricted) Max-CSPs, which generalises both bounded MIM-width and β-acyclicity. On the way, we give a new characterisation of bounded MIM-width and discuss other hypergraph properties which are relevant to the complexity of Max-CSPs, such as β-hypertreewidth

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

The Meta-Problem for Conservative Mal'tsev Constraints

International audienceIn the algebraic approach to CSP (Constraint Satisfaction Problem), the complexity of constraint languages is studied using closure operations called poly-morphisms. Many of these operations are known to induce tractability of any language they preserve. We focus on the meta-problem: given a language Γ, decide if Γ has a polymorphism with nice properties. We design an algorithm that decides in polynomial-time if a constraint language has a conservative Mal'tsev poly-morphism, and outputs one if one exists. As a corollary we obtain that the class of conservative Mal'tsev constraints is uniformly tractable, and we conjecture that this result remains true in the non-conservative case

Exploiter la traçabilité dans les problèmes de satisfaction des contraintes

The Constraint Satisfaction Problem (CSP) is a fundamental NP-complete problem with many applications in artificial intelligence. This problem has enjoyed considerable scientific attention in the past decades due to its practical usefulness and the deep theoretical questions it relates to. However, there is a wide gap between practitioners, who develop solving techniques that are efficient for industrial instances but exponential in the worst case, and theorists who design sophisticated polynomial-time algorithms for restrictions of CSP defined by certain algebraic properties. In this thesis we attempt to bridge this gap by providing polynomial-time algorithms to test for membership in a selection of major tractable classes. Even if the instance does not belong to one of these classes, we investigate the possibility of decomposing efficiently a CSP instance into tractable subproblems through the lens of parameterized complexity. Finally, we propose a general framework to adapt the concept of kernelization, central to parameterized complexity but hitherto rarely used in practice, to the context of constraint reasoning. Preliminary experiments on this last contribution show promising results.Le problème de satisfaction de contraintes (CSP) est un problème NP-complet classique en intelligence artificielle qui a suscité un engouement important de la communauté scientifique grâce à la richesse de ses aspects pratiques et théoriques. Cependant, au fil des années un gouffre s’est creusé entre les praticiens, qui développent des méthodes exponentielles mais efficaces pour résoudre des instances industrielles, et les théoriciens qui conçoivent des algorithmes sophistiqués pour résoudre en temps polynomial certaines restrictions de CSP dont l’intérˆet pratique n’est pas avéré. Dans cette thèse nous tentons de réconcilier les deux communaut és en fournissant des méthodes polynomiales pour tester automatiquement l’appartenance d’une instance de CSP à une sélection de classes traitables majeures. Anticipant la possibilité que les instances réelles ne tombent que rarement dans ces classes traitables, nous analysons également de manière systématique la possibilité de décomposer efficacement une instance en sous-problèmes traitables en utilisant des méthodes de complexité paramétrée. Finalement, nous introduisons un cadre général pour exploiter dans les CSP les idées développées pour la kernelization, un concept fondamental de complexité paramétrée jusqu’ici peu utilisé en pratique. Ce dernier point est appuyé par des expérimentations prometteuses

On the Kernelization of Global Constraints

International audienceKernelization is a powerful concept from parameterized complexity theory that captures (a certain idea of) efficient polynomial-time preprocessing for hard decision problems. However, exploiting this technique in the context of constraint programming is challenging. Building on recent results for the VERTEXCOVER constraint, we introduce novel "loss-less" kernelization variants that are tailored for constraint propagation. We showcase the theoretical interest of our ideas on two constraints, VERTEXCOVER and EDGEDOMINATINGSET

Chain Length and CSPs Learnable with Few Queries

International audienceThe goal of constraint acquisition is to learn exactly a constraint network given access to an oracle that answers truthfully certain types of queries. In this paper we focus on partial membership queries and initiate a systematic investigation of the learning complexity of constraint languages. First, we use the notion of chain length to show that a wide class of languages can be learned with as few as O(n log(n)) queries. Then, we combine this result with generic lower bounds to derive a dichotomy in the learning complexity of binary languages. Finally, we identify a class of ternary languages that eludes our framework and hints at new research directions

Tractable Explaining of Multivariate Decision Trees

International audienceWe study multivariate decision trees (MDTs), in particular, classes of MDTs determined by the language of relations that can be used to split feature space. An abductive explanation (AXp) of the classification of a particular instance, viewed as a set of feature-value assignments, is a minimal subset of the instance which is sufficient to lead to the same decision. We investigate when finding a single AXp is tractable. We identify tractable languages for real, integer and boolean features. Indeed, in the case of boolean languages, we provide a P/NP-hard dichotomy

On Singleton Arc Consistency for CSPs Defined by Monotone Patterns

International audienceSingleton arc consistency is an important type of local consistency which has been recently shown to solve all constraint satisfaction problems (CSPs) over constraint languages of bounded width. We aim to characterise all classes of CSPs defined by a forbidden pattern that are solved by singleton arc consistency and closed under removing constraints. We identify five new patterns whose absence ensures solvability by singleton arc consistency, four of which are provably maximal and three of which generalise 2-SAT. Combined with simple counterexamples for other patterns, we make significant progress towards a complete classification

Q-intersection Algorithms for Constraint-Based Robust Parameter Estimation

International audienceGiven a set of axis-parallel n-dimensional boxes, the q-intersection is defined as the smallest box encompassing all the points that belong to at least q boxes. Computing the q-intersection is a combinatorial problem that allows us to han-dle robust parameter estimation with a numerical constraint programming approach. The q-intersection can be viewed as a filtering operator for soft constraints that model measure-ments subject to outliers. This paper highlights the equiva-lence of this operator with the search of q-cliques in a graph whose boxicity is bounded by the number of variables in the constraint network. We present a computational study of the q-intersection. We also propose a fast heuristic and a sophisti-cated exact q-intersection algorithm. First experiments show that our exact algorithm outperforms the existing one while our heuristic performs an efficient filtering on hard problems