Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

Publisher Summary This chapter explores that there are three main algorithmic techniques
for solving constraint satisfaction problems:(1) backtracking search,(2) local search, and (3) …

This is the first of two papers presenting and evaluating the power of a new framework for
combinatorial optimization in graphical models, based on AND/OR search spaces. We …

Abstract Inference in Bayes Nets (BAYES) is an important problem with numerous
applications in probabilistic reasoning. Counting the number of satisfying assignments of a …

Methods exploiting tree-decompositions seem to provide the best approach for solving
constraint networks wrt the theoretical time complexity. However, they have not shown a real …

Portfolio-based solvers are both effective and robust, but their promise for parallel execution
with constraint satisfaction solvers has received relatively little attention. This paper …

We show a method resulting in the improvement of several polynomial-space, exponential-
time algorithms. The method capitalizes on the existence of small balanced separators for …

The need to model and solve constraints over large sets of relational data occurs frequently
in practice. Naively and inefficiently, solutions to the problem may be implemented in ad-hoc …

In this paper, we address two key aspects of solving methods based on tree-decomposition.
First, we propose an algorithm computing decompositions that allows to bound the size of …

Dividing a Boolean formula into smaller independent sub-formulae can be a useful
technique for accelerating the solution of Boolean problems, including SAT and# SAT …