On Dual Encodings for Non-binary Constraint Satisfaction Problems

Nagarajan, Sivakumar; Goodwin, Scott D.; Sattar, Abdul; Thornton, John

doi:10.1007/3-540-45349-0_44

Cited by 3 publications

(2 citation statements)

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“…By extending the algorithm for Dual arc consistency given earlier we can get an algorithm for enforcing -consistency. In (Nagarajan et al 2000) this was generalised to generalised dual arc consistency.…”

Section: Generalised Dual Arc Consistencymentioning

confidence: 99%

“…In some cases the space complexity of the dual encodings is prohibitive and this is sometimes a drawback when trying to use these encodings. More recently (Nagarajan et al 2000) modifications to the standard dual encoding have been proposed that can compactly represent the given CSP using an equivalent dual encoding that contains all the original solutions to the CSP, using constraint coverings. It has also been shown that enforcing arc consistency in these constraint covering based encodings, strictly dominates enforcement of GAC on the primal nonbinary encoding.…”

Section: Introductionmentioning

confidence: 99%

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Extending Dual Arc Consistency

Nagarajan

Goodwin

Sattar

2003

Int. J. Patt. Recogn. Artif. Intell.

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Comparisons between primal and dual approaches have recently been extensively studied and evaluated from a theoretical standpoint based on the amount of pruning achieved by each of these when applied to non-binary constraint satisfaction problems. Enforcing arc consistency on the dual encoding has been shown to strictly dominate enforcing GAC on the primal encoding (Stergiou & Walsh 1999). More recently, extensions to dual arc consistency have extended these results to dual encodings that are based on the construction of compact constraint coverings, that retain the completeness of the encodings, while using a fraction of the space. In this paper we present a complete theoretical evaluation of these different consistency techniques and also demonstrate how arbitrarily high levels of consistency can be achieved efficiently using them.

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Section: Generalised Dual Arc Consistencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%