Exploring Directional Path-Consistency for Solving Constraint Networks

TL;DR

This paper introduces DPC*, an efficient variant of directional path-consistency, which can decide certain classes of constraint satisfaction problems and enable backtrack-free search, outperforming existing algorithms in practical applications.

Contribution

The paper proposes DPC*, a new algorithm that extends directional path-consistency to solve majority-closed constraint networks efficiently and guarantees backtrack-free search.

Findings

DPC* outperforms state-of-the-art algorithms in experiments.

DPC* guarantees backtrack-free search for majority-closed constraints.

DPC* can decide CSPs in classes like CRC and tree-preserving constraints.

Abstract

Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention. However, enforcing PC is computationally expensive and sometimes even unnecessary. Directional path-consistency (DPC) is a weaker notion of PC that considers a given variable ordering and can thus be enforced more efficiently than PC. This paper shows that DPC (the DPC enforcing algorithm of Dechter and Pearl) decides the constraint satisfaction problem (CSP) of a constraint language if it is complete and has the variable elimination property (VEP). However, we also show that no complete VEP constraint language can have a domain with more than 2 values. We then present a simple variant of the DPC algorithm, called DPC*, and show that the CSP of a constraint language can be decided by DPC* if it is closed under a majority operation. In fact, DPC* is sufficient for guaranteeing backtrack-free search for such constraint networks. Examples of majority-closed constraint classes include the classes of connected row-convex (CRC) constraints and tree-preserving constraints, which have found applications in various domains, such as scene labeling, temporal reasoning, geometric reasoning, and logical filtering. Our experimental evaluations show that DPC* significantly outperforms the state-of-the-art algorithms for solving majority-closed constraints.