This work aims towards the automatic generation of advice to guide the solution of difficult
constraintsatisfaction problems (CSPs). The advice is generated by consulting relaxed, easy …

A major challenge in constraint programming is to develop efficient generic approaches to
solve instances of the constraint satisfaction problem (CSP). With this aim in mind, this book …

Constraint satisfaction has been used as a term to cover a wide range of methods to solve
problems stated in the form of a set of constraints. As the general constraint satisfaction …

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 …

A constraint satisfaction problem instance consists of a collection of variables that need to
have values assigned to them. The assignments are limited by constraints that force the …

The choice of a particular algorithm for solving a given class of constraint satisfaction
problems is often confused by exceptional behaviour of algorithms. One method of reducing …

Conventional techniques for the constraint satisfaction problem (CSP) have had
considerable success in their applications. However, there are many areas in which the …

Partial constraint satisfaction [5] was widely studied in the 90's, and notably Max-CSP
solving algorithms [21, 20, 1, 10]. These algorithms compute a lower bound of violated …

The performance of constraint based problem solving depends crucially on the problem
representation. The same problem may be easy or di cult to solve depending on the way we …

The algorithm presented here, BCC, is an enhancement of the well known Backtrack used to
solve constraint satisfaction problems. Though most backtrack improvements rely on …