Generalized Majority-Minority Operations are Tractable

Generalized majority-minority (GMM) operations are introduced as a common generalization of near unanimity operations and Mal'tsev operations on finite sets. We show that every instance of the constraint satisfaction problem (CSP), where all constraint relations are invariant under a (fixed) GMM operation, is solvable in polynomial time. This constitutes one of the largest tractable cases of the CSP

Solving Functional Constraints by Variable Substitution

Functional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ CSP-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other non-functional constraints. It also solves globally any CSP where there exists a variable such that any other variable is reachable from it through a sequence of functional constraints. Our experiments on random problems show that variable elimination can significantly improve the efficiency of solving problems with functional constraints

Linear Datalog and Bounded Path Duality of Relational Structures

In this paper we systematically investigate the connections between logics with a finite number of variables, structures of bounded pathwidth, and linear Datalog Programs. We prove that, in the context of Constraint Satisfaction Problems, all these concepts correspond to different mathematical embodiments of a unique robust notion that we call bounded path duality. We also study the computational complexity implications of the notion of bounded path duality. We show that every constraint satisfaction problem \csp(\best) with bounded path duality is solvable in NL and that this notion explains in a uniform way all families of CSPs known to be in NL. Finally, we use the results developed in the paper to identify new problems in NL

Tractable constraints on ordered domains

AbstractFinding solutions to a constraint satisfaction problem is known to be an NP-complete problem in general, but may be tractable in cases where either the set of allowed constraints or the graph structure is restricted. In this paper we identify a restricted set of contraints which gives rise to a class of tractable problems. This class generalizes the notion of a Horn formula in propositional logic to larger domain sizes. We give a polynomial time algorithm for solving such problems, and prove that the class of problems generated by any larger set of constraints is NP-complete

A Graph Based Backtracking Algorithm for Solving General CSPs

Many AI tasks can be formalized as constraint satisfaction problems (CSPs), which involve finding values for variables subject to constraints. While solving a CSP is an NP-complete task in general, tractable classes of CSPs have been identified based on the structure of the underlying constraint graphs. Much effort has been spent on exploiting structural properties of the constraint graph to improve the efficiency of finding a solution. These efforts contributed to development of a class of CSP solving algorithms called decomposition algorithms. The strength of CSP decomposition is that its worst-case complexity depends on the structural properties of the constraint graph and is usually better than the worst-case complexity of search methods. Its practical application is limited, however, since it cannot be applied if the CSP is not decomposable. In this paper, we propose a graph based backtracking algorithm called omega-CDBT, which shares merits and overcomes the weaknesses of both decomposition and search approaches

A review of literature on parallel constraint solving

As multicore computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multicore computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation

Le problème de décision CSP : homomorphismes et espace logarithmique

Ce mémoire porte sur le problème de décision CSP (de l'anglais Constraint Satisfaction Problem, c'est-à-dire problème de satisfaction de contraintes), soit le problème pour lequel nous devons assigner des valeurs à des variables de telle sorte que toutes les conditions portant sur ces variables soient remplies. De surcroît, ce mémoire porte sur les problèmes de détection d'homomorphisme entre structures relationelles qui sont équivalents à CSP. Pour être plus précis, nous nous intéressons à l'algorithme de cohérence d'arc pour les instances de CSP, soit ArcjConsistency. Celui-ci suffit à solutionner un certain sous-ensemble de CSP. Or nous étudions quelques-unes de ses variantes qui sont des algorithmes plus coûteux, mais plus puissants, c'est-à-dire que le sous-ensemble de CSP qu'ils solutionnent est plus grand. La nouveauté de ce mémoire est de décrire et d'étudier une variante de ArcjConsistency, soit NLjCohérence, qui est un algorithme moins puissant mais plus efficace. L'objectif pour nous est de trouver des caractéristiques intéressantes au sujet de ce nouvel algorithme, qui se veut être une version « espace logarithmique » de ArcjConsistency. De plus, nous travaillons sur un sous-ensemble de CSP dit implicatif. Nous démontrons que NL_Cohérence solutionne les instances de ce sous-ensemble en espace logarithmique non-déterministe