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An MDD-based generalized arc consistency algorithm for positive and negative table constraints and some global constraints. (English) Zbl 1204.68188
Summary: A table constraint is explicitly represented as its set of solutions or non-solutions. This ad hoc (or extensional) representation may require space exponential to the arity of the constraint, making enforcing GAC expensive. In this paper, we address the space and time inefficiencies simultaneously by presenting the MDDC constraint. MDDC is a global constraint that represents its (non-)solutions with a Multi-Valued Decision Diagram (MDD). The MDD-based representation has the advantage that it can be exponentially smaller than a table. The associated GAC algorithm (called MDDC) has time complexity linear to the size of the MDD, and achieves full incrementality in constant time. In addition, we show how to convert a positive or negative table constraint into an MDDC constraint in time linear to the size of the table. Our experiments on structured problems, car sequencing and still-life, show that MDDC is also a fast GAC algorithm for some global constraints such as sequence and regular. We also show that MDDC is faster than the state-of-the-art generic GAC algorithms in [I. P. Gent, C. Jefferson, I. Miguel and P. Nightingale, “Data structures for generalized arc consistency for extensional constraints”, in: National conference on artificial intelligence (2007)], [C. Lecoutre and R. Szymanek, “Generalized arc consistency for positive table constraints”, in: International conference on principles and practice of constraint programming (2006)], and [O. Lhomme and J. C. Régin, “A fast arc consistency algorithm for \(n\)-ary constraints”, in: National conference on artificial intelligence (2005)] for table constraint.

MSC:
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
Software:
MiniSat; SICStus
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