Weighted Model Counting with Algebraic Decision Diagrams

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dc.contributor.advisorVardi, Mosheen_US
dc.creatorPhan, Vu Hoang Nguyenen_US
dc.date.accessioned2019-12-04T21:42:22Zen_US
dc.date.available2019-12-04T21:42:22Zen_US
dc.date.created2019-12en_US
dc.date.issued2019-12-04en_US
dc.date.submittedDecember 2019en_US
dc.date.updated2019-12-04T21:42:22Zen_US
dc.description.abstractWe present an algorithm to compute exact literal-weighted model counts of Boolean formulas in conjunctive normal form. Our algorithm employs dynamic programming and uses algebraic decision diagrams as the primary data structure. We implement this technique in ADDMC, a new model counter. We empirically evaluate various heuristics that can be used with ADDMC. We then compare ADDMC to state-of-the-art exact weighted model counters (Cachet, c2d, d4, and miniC2D) on 1914 standard model counting benchmarks and show that ADDMC significantly improves the virtual best solver.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationPhan, Vu Hoang Nguyen. "Weighted Model Counting with Algebraic Decision Diagrams." (2019) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/107761">https://hdl.handle.net/1911/107761</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/107761en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectknowledge compilationen_US
dc.subjectfactored representationen_US
dc.subjectheuristicsen_US
dc.titleWeighted Model Counting with Algebraic Decision Diagramsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.majorComputational Logicen_US
thesis.degree.nameMaster of Scienceen_US
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