Partiall (Neighbourhood) Singleton Arc Consistency for Constraint Satisfaction Problems

Publication Type:
Refereed Original Article

Abstract:
Algorithms based on singleton arc consistency (SAC) show considerable promise for improving backtrack search algorithms for constraint satisfaction problems (CSPs). The drawback is that even the most efficient of them is still comparatively expensive. Even when limited to preprocessing, they give overall improvement only when problems are quite difficult to solve with more typical procedures such as maintained arc consistency (MAC). The present work examines a form of partial SAC and neighbourhood SAC (NSAC) in which a subset of the variables in a CSP is chosen to be made SAC-consistent or neighbourhood-SAC-consistent. Such consistencies, despite their partial character, are still well-characterized in that algorithms have unique fixpoints. Heuristic strategies for choosing an effective subset of variables are described and tested, the best being choice by highest degree and a more complex strategy of choosing by constraint weight after random probing. Experimental results justify the claim that these methods can be nearly as effective as the corresponding full version of the algorithm in terms of values discarded or problems proven unsatisfiable, while significantly reducing the effort required to achieve this.

Digital Object Identifer (DOI):
10.000.000

Publication Status:
In Press

Date Accepted for Publication:
Wednesday, 11 March, 2020

Publication Date:
01/09/2020

Journal:
Fundamenta Informaticae

Research Group:
Unknown

Institution:
National University of Ireland, Cork (UCC)

Open access repository:
No