New Advances in Compiling CNF into Decomposable Negation Normal Form

By Adnan Darwiche. European Conference on Artificial Intelligence, 2004

312 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/11e0371684e690d51f109d63a3b8142b84194415

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Citations in the corpus

Three Modern Roles for Logic in AI (2020, methodology): “s. In Section 3, we will further show how these problems (of numeric nature) can be competitively solved using purely symbolic manipulations. 3Some of the popular or traditional model counters are c2d[26], mini-c2d[63], d4[46], cache [73], sharp-sat [88], sdd [12] and dsharp [56]. Many of these syst”
On probabilistic inference by weighted model counting (2008, methodology): “In addition, advanced techniques such as clause learning, component caching, and non–chronological backtracking, are used to improve eﬃciency, but we do not detail them here; see [18,31,26,32].”
Probabilistic Inference in Hybrid Domains by Weighted Model Integration (2015, methodology): “Exact WMC solvers are based on knowledge compilation [Darwiche, 2004; Muise et al., 2012] or exhaustive DPLL search [Sang et al.”
Open-Universe Weighted Model Counting (2017, methodology): “For a range of problems, we have verified that correct answers wrt the OU semantics are returned using the C2D WMC solver (Darwiche 2004), the WFOMC lifted WMC solver (Van den Broeck 2013), and the ProbLog probabilistic programming language (Fierens et al. 2011).6 In this section, we investigate…”
Component Caching in Hybrid Domains with Piecewise Polynomial Densities (2016, methodology): “Overall, CC is the dominant approach for solving #SAT exactly, directly by DPLL search or through knowledge compilation (Darwiche 2004).”
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains (2015, methodology): “Exact WMC solvers are based on knowledge compilation (Darwiche, 2004; Muise et al., 2012) or DPLL search with component caching (Sang et al.”