A Tagging Method for Parallel Constraint Satisfaction

Abstract Local propagation algorithms such as Waltz' filtering and Mackworth's AC-x algorithms have been successfully applied in artificial intelligence for solving constraint satisfaction problems (CSPs). In general, these algorithms can only be used as preprocessing methods as they do not compute a globally consistent solution for a CSP; they result in local consistency, also known as arc consistency. In this paper, we introduce an extension of local constraint propagation to overcome this drawback, i.e., to compute globally consistent solutions for a CSP. The underlying idea is to associate recursive tags with the values during the propagation process so that global relationships among the values are maintained. Our method has a straightforward implementation on shared-memory multiprocessors.