Existing random models for the constraint satisfaction problem (CSP) all require an
extremely low constraint tightness in order to have non-trivial threshold behaviors and …

In this paper, we study the possibility of designing non-trivial random CSP models by
exploiting the intrinsic connection between structures and typical-case hardness. We show …

Recently there has been a great amount of interest in Random Constraint Satisfaction
Problems, both from an experimental and a theoretical point of view. Rather intriguingly …

The constraint satisfaction problem (CSP) is a fundamental problem in Artificial Intelligence,
with applications ranging from scene labelling to scheduling and knowledge representation …

In this paper, we study the relation among the parameters in their most general setting that
define a large class of random CSP models dk-CSP where d is the domain size and k is the …

In the last few years there has been a great amount of interest in Random Constraint
Satisfaction Problems, both from an experimental and a theoretical point of view. Quite …

Randomly-generated binary constraint satisfaction problems go through a phase transition
as the constraint tightness varies. Loose constraints give an 'easy-soluble'region, where …

Constraints satisfaction problem (CSP) is a family of computation problems that are
generally hard to solve in the worst case, which motivates the study of average cases by …

We consider random instances of constraint satisfaction problems where each variable has
domain size O (1), each constraint is on O (1) variables and the constraints are chosen from …

The paper presents a method for generating solutions of a constraint satisfaction problem
(CSP) uniformly at random. Our method relies on expressing the constraint network as a …