---
title: "Exploiting Short Supports for Generalised Arc Consistency for Arbitrary Constraints"
authors:
  - "Peter Nightingale"
  - "Ian P. Gent"
  - "Christopher Jefferson"
  - "Ian Miguel"
doi: "10.5591/978-1-57735-516-8/IJCAI11-111"
url: "https://ijcai.org/proceedings/2011/111"
pdf: "https://ijcai.org/proceedings/2011/0111.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "623-627"
track: ""
keywords:
---

# Exploiting Short Supports for Generalised Arc Consistency for Arbitrary Constraints

Peter Nightingale, Ian P. Gent, Christopher Jefferson, Ian Miguel

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 623-627. DOI: [10.5591/978-1-57735-516-8/IJCAI11-111](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-111)

## 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.