The standard models used to generate random binary constraint satisfaction problems are
described. At the problem sizes studied experimentally, a phase transition is seen as the …

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

We study two natural models of randomly generated constraint satisfaction problems. We
determine how quickly the domain size must grow with n to ensure that these models are …

An empirical study of randomly generated binary constraint satisfaction problems reveals
that for problems with a given number of variables, domain size, and connectivity there is a …

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

We introduce a class of models for random Constraint Satisfaction Problems. This class
includes and generalizes many previously studied models. We characterize those models …

A recent theoretical result by Achlioptas et al. shows that many models of random binary
constraint satisfaction problems become trivially insoluble as problem size increases. This …

The phase transition in binary constraint satisfaction problems, ie the transition from a region
in which almost all problems have many solutions to a region in which almost all problems …

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 …

We consider a random constraint satisfaction problem named model RB, which exhibits a
sharp satisfiability phase-transition phenomenon when the control parameters pass through …