Title: A relaxed Tseitin transformation for weighted model counting
Authors: Meert, Wannes
Vlasselaer, Jonas
Van den Broeck, Guy
Issue Date: 11-Jul-2016
Host Document: Proceedings of the Sixth International Workshop on Statistical Relational AI (StarAI) pages:1-7
Conference: Workshop on Statistical Relational AI edition:6 location:NYC date:11 July 2016
Abstract: The task of Weighted Model Counting is to compute the sum of the weights of all satisfying assign- ments of a propositional sentence. One recent key insight is that, by allowing negative weights, one can restructure the sentence to obtain a representation that allows for more efficient counting. This has been shown for formulas representing Bayesian networks with noisy-OR structures (Vomlel and Savicky 2008; Li, Poupart, and van Beek 2011) and for first-order model counting (Van den Broeck, Meert, and Darwiche 2014). In this work, we introduce the relaxed Tseitin transformation and show that the aforementioned techniques are special cases of this relaxation.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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