Collaborating Authors

Efficient Algorithms for Strong Local Consistencies in Constraint Satisfaction Problems

AAAI Conferences

The existing complete methods for solving Constraint Satisfaction Problems (CSPs) are usually based on a combination of exhaustive search and constraint propagation techniques for the reduction of the search space. Such propagation techniques are the local consistency algorithms. Arc Consistency (AC) and Generalized Arc Consistency (GAC) are the most widely studied local consistencies that are predominantly used in constraint solvers. However, many stronger local consistencies than (G)AC have been proposed, even recently, but have been rather overlooked due to their prohibitive cost. This research proposes efficient algorithms for strong consistencies for both binary and non-binary constraints that can be easily adopted by standard CP solvers. Experimental results have so far demonstrated that the proposed algorithms are quite competitive and often more efficient than state-of-the-art methods, being orders of magnitude faster on various problem classes.


AAAI Conferences

We propose two local consistencies that extend bounds consistency (BC) by simultaneously considering combinations of constraints as opposed to single constraints. We prove that these two local consistencies are both stronger than BC, but are NP-hard to enforce even when constraints are linear. Hence, we propose two polynomial-time techniques to enforce approximations of these two consistencies on linear constraints. One is a reformulation of the constraints on which we enforce BC whereas the other is a polynomial time algorithm. Both achieve stronger pruning than BC. Our experiments show large differences in favor of our approaches.

Local Consistency in Junction Graphs for Constraint-Based Inference

AAAI Conferences

The concept of local consistency plays a central role in constraint satisfaction. Given a constraint satisfaction problem (CSP), local consistency can be characterized as deriving new, possibly tighter, constraints based on local information. The derived constraints simplify the representation of the original CSP without the loss of solutions. This can be seen as a preprocessing procedure. Based on arc consistency (Mackworth.

Domain Filtering Consistencies

Journal of Artificial Intelligence Research

Enforcing local consistencies is one of the main features of constraint reasoning. Which level of local consistency should be used when searching for solutions in a constraint network is a basic question. Arc consistency and partial forms of arc consistency have been widely studied, and have been known for sometime through the forward checking or the MAC search algorithms. Until recently, stronger forms of local consistency remained limited to those that change the structure of the constraint graph, and thus, could not be used in practice, especially on large networks. This paper focuses on the local consistencies that are stronger than arc consistency, without changing the structure of the network, i.e., only removing inconsistent values from the domains. In the last five years, several such local consistencies have been proposed by us or by others. We make an overview of all of them, and highlight some relations between them. We compare them both theoretically and experimentally, considering their pruning efficiency and the time required to enforce them.


AAAI Conferences

In this paper, we introduce a new class of local consistencies, called df-consistencies, for qualitative constraint networks. Each consistency of this class is based on weak composition and a mapping f that provides a covering for each relation of the considered qualitative calculus. We study the connections existing between some properties of the introduced mappings and the relative inference strength of df-consistencies. The consistency obtained by the usual closure under weak composition corresponds to the weakest element of the class, whereas df-consistencies stronger than weak composition open new promising perspectives. Interestingly, the class of df-consistencies is shown to form a complete lattice where the partial order denotes the relative strength of every two consistencies. We also propose a generic algorithm that allows us to compute the closure of qualitative constraint networks under any "well-behaved" consistency of the class. The experimentation that we have conducted on qualitative constraint networks from the Interval Algebra shows the interest of these new local consistencies, in particular for the consistency problem.