Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-030-81688-9_15
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dc.titleCounting Minimal Unsatisfiable Subsets
dc.contributor.authorBendík, J.
dc.contributor.authorMeel, Kuldeep S.
dc.date.accessioned2022-10-13T08:13:15Z
dc.date.available2022-10-13T08:13:15Z
dc.date.issued2021-01-01
dc.identifier.citationBendík, J., Meel, Kuldeep S. (2021-01-01). Counting Minimal Unsatisfiable Subsets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12760 LNCS : 313-336. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-030-81688-9_15
dc.identifier.isbn9783030816872
dc.identifier.issn0302-9743
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/233330
dc.description.abstractGiven an unsatisfiable Boolean formula F in CNF, an unsatisfiable subset of clauses U of F is called Minimal Unsatisfiable Subset (MUS) if every proper subset of U is satisfiable. Since MUSes serve as explanations for the unsatisfiability of F, MUSes find applications in a wide variety of domains. The availability of efficient SAT solvers has aided the development of scalable techniques for finding and enumerating MUSes in the past two decades. Building on the recent developments in the design of scalable model counting techniques for SAT, Bendík and Meel initiated the study of MUS counting techniques. They succeeded in designing the first approximate MUS counter, AMUSIC, that does not rely on exhaustive MUS enumeration. AMUSIC, however, suffers from two shortcomings: the lack of exact estimates and limited scalability due to its reliance on 3-QBF solvers. In this work, we address the two shortcomings of AMUSIC by designing the first exact MUS counter, CountMUST, that does not rely on exhaustive enumeration. CountMUST circumvents the need for 3-QBF solvers by reducing the problem of MUS counting to projected model counting. While projected model counting is #NP-hard, the past few years have witnessed the development of scalable projected model counters. An extensive empirical evaluation demonstrates that CountMUST successfully returns MUS count for 1500 instances while AMUSIC and enumeration-based techniques could only handle up to 833 instances. © 2021, The Author(s).
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.typeConference Paper
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.doi10.1007/978-3-030-81688-9_15
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume12760 LNCS
dc.description.page313-336
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