Abstract

We consider the Weighted Constraint Satisfaction Problem which is an important problem in Artificial Intelligence. Given a set ofvariables, their domains and a set ofconstraints between variables, our goal is to obtain an assignment ofthe variables to domain values such that the weighted sum ofsatisfied constraints is maximized. In this paper, we present a new approach based on randomized rounding ofsemidefinite programming relaxation. Besides having provable worst-case bounds for domain sizes 2 and 3, our algorithm is simple and efficient in practice, and produces better solutions than some other polynomial-time algorithms such as greedy and randomized local search.

Citations