Exploiting short supports for generalised arc consistency for arbitrary constraints

Authors:
Peter Nightingale;Ian P. Gent;Chris Jefferson;Ian Miguel
Affiliations:
School of Computer Science, University of St Andrews, St Andrews, UK;School of Computer Science, University of St Andrews, St Andrews, UK;School of Computer Science, University of St Andrews, St Andrews, UK;School of Computer Science, University of St Andrews, St Andrews, UK
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Year:
2011

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Abstract

Special-purpose constraint propagation algorithms (such as those for the element constraint) frequently make implicit use of short supports - by examining a subset of the variables, they can infer support for all other variables and values and save substantial work. However, to date general purpose propagation algorithms (such as GAC-Schema) rely upon supports involving all variables. We demonstrate how to employ short supports in a new general purpose propagation algorithm called SHORTGAC. This works when provided with either an explicit list of allowed short tuples, or a function to calculate the next supporting short tuple. Empirical analyses demonstrate the efficiency of SHORTGAC compared to other general-purpose propagation algorithms. In some cases SHORTGAC even exhibits similar performance to special-purpose propagators.