Extending Compact-Diagram to Basic Smart Multi-Valued Variable Diagrams

Verhaeghe, Hélène; Lecoutre, Christophe; Schaus, Pierre

doi:10.1007/978-3-030-19212-9_39

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…In order to reduce the gap, Verhaeghe et al proposed the MDD GAC algorithm CD (Verhaeghe, Lecoutre, and Schaus 2018) using ideas from CT. The CD algorithm has been also extended with some alternative representations of MDDs, such as semi-MDDs (Verhaeghe, Lecoutre, and Schaus 2018) and basic smart MVDs (Verhaeghe, Lecoutre, and Schaus 2019). Although CD and its variants can be faster than the MDD4R algorithm and reduce the gap, CT still overall outperforms CD and its variants on a large set of benchmarks (see the experimental results in Schaus 2018, 2019)).…”

Section: Related Workmentioning

confidence: 99%

Encoding Multi-Valued Decision Diagram Constraints as Binary Constraint Trees

Wang¹,

Yap²

2022

AAAI

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Ordered Multi-valued Decision Diagram (MDD) is a compact representation used to model various constraints, such as regular constraints and table constraints. It can be particularly useful for representing ad-hoc problem specific constraints. Many algorithms have been proposed to enforce Generalized Arc Consistency (GAC) on MDD constraints. In this paper, we introduce a new compact representation called Binary Constraint Tree (BCT). We propose tree binary encodings to transform any MDD constraint into a BCT constraint. We also present a specialized algorithm enforcing GAC on the BCT constraint resulting from a MDD constraint. Experimental results on a large set of benchmarks show that the BCT GAC algorithm can significantly outperform state-of-the-art MDD as well as table GAC algorithms.

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Section: Related Workmentioning

confidence: 99%