Compiling Constraint Networks into Multivalued Decomposable Decision Graphs

Specifically, we present a top-down algorithm cn2mddg for compiling finite-domain CNs into multivalued decomposable We present and evaluate a top-down algorithm for decision graphs. The input of cn2mddg is a CN compiling finite-domain constraint networks (CNs) represented in the XCSP 2.1 format [Roussel and Lecoutre, into the language MDDG of multivalued decomposable 2009]. The output of our compilation algorithm is a representation decision graphs. Though it includes Decision-of the solutions of the CN in the language MDDG DNNF as a proper subset, MDDG offers the same key of multivalued decomposable decision graphs. MDDG is precisely tractable queries and transformations as Decision-the extension to non-Boolean domains of the language DNNF, which makes it useful for many applications. DDG [Fargier and Marquis, 2006] also known as Decision-Intensive experiments showed that our compiler DNNF [Oztok and Darwiche, 2014]: it is based on decomposable cn2mddg succeeds in compiling CNs which -nodes and (multivalued) decision nodes. Similarly are out of the reach of standard approaches based to Decision-DNNF, the MDDG language offers a number of on a translation of the input network to CNF, followed tractable queries, including (possibly weighted) solution finding by a compilation to Decision-DNNF. Furthermore, and counting, solution enumeration (solutions can be enumerated the sizes of the resulting compiled representations with polynomial delay), and optimization w.r.t. a linear turn out to be much smaller (sometimes by objective function. It also offers tractable transformations, several orders of magnitude).