Dsharp: fast d-DNNF compilation with sharpSAT

Authors:
Christian Muise;Sheila A. McIlraith;J. Christopher Beck;Eric I. Hsu
Affiliations:
Department of Computer Science, University of Toronto, Toronto, Canada;Department of Computer Science, University of Toronto, Toronto, Canada;Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Canada;Department of Computer Science, University of Toronto, Toronto, Canada
Venue:
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

Knowledge compilation is a compelling technique for dealing with the intractability of propositional reasoning. One particularly effective target language is Deterministic Decomposable Negation Normal Form (d-DNNF). We exploit recent advances in #SAT solving in order to produce a new state-of-the-art CNF → d-DNNF compiler: Dsharp. Empirical results demonstrate that Dsharp is generally an order of magnitude faster than c2d, the de facto standard for compiling to d-DNNF, while yielding a representation of comparable size.