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Compiling Constraint Networks into Multivalued Decomposable Decision Graphs

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 332-337.

Abstract

We present and evaluate a top-down algorithm for compiling finite-domain constraint networks (CNs) into the language MDDG of multivalued decomposable decision graphs. Though it includes Decision-DNNF as a proper subset, MDDG offers the same key tractable queries and transformations as Decision-DNNF, which makes it useful for many applications. Intensive experiments showed that our compiler cn2mddg succeeds in compiling CNs which are out of the reach of standard approaches based on a translation of the input network to CNF, followed by a compilation to Decision-DNNF. Furthermore, the sizes of the resulting compiled representations turn out to be much smaller (sometimes by several orders of magnitude).

Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis, and Samuel Thomas. 2015. Compiling Constraint Networks into Multivalued Decomposable Decision Graphs. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 332–337. https://ijcai.org/proceedings/2015/53
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