Academic literature on the topic 'Quantified Boolean Formulas'

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Journal articles on the topic "Quantified Boolean Formulas"

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Buning, H. K., M. Karpinski, and A. Flogel. "Resolution for Quantified Boolean Formulas." Information and Computation 117, no. 1 (1995): 12–18. http://dx.doi.org/10.1006/inco.1995.1025.

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Bubeck, Uwe, and Hans Kleine Büning. "Encoding Nested Boolean Functions as Quantified Boolean Formulas." Journal on Satisfiability, Boolean Modeling and Computation 8, no. 1-2 (2012): 101–16. http://dx.doi.org/10.3233/sat190092.

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Kleine Büning, Hans, K. Subramani, and Xishun Zhao. "Boolean Functions as Models for Quantified Boolean Formulas." Journal of Automated Reasoning 39, no. 1 (2007): 49–75. http://dx.doi.org/10.1007/s10817-007-9067-0.

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Zhang, Pan, Abolfazl Ramezanpour, Lenka Zdeborová, and Riccardo Zecchina. "Message passing for quantified Boolean formulas." Journal of Statistical Mechanics: Theory and Experiment 2012, no. 05 (2012): P05025. http://dx.doi.org/10.1088/1742-5468/2012/05/p05025.

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Samer, Marko, and Stefan Szeider. "Backdoor Sets of Quantified Boolean Formulas." Journal of Automated Reasoning 42, no. 1 (2008): 77–97. http://dx.doi.org/10.1007/s10817-008-9114-5.

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Kauers, Manuel, and Martina Seidl. "Short proofs for some symmetric Quantified Boolean Formulas." Information Processing Letters 140 (December 2018): 4–7. http://dx.doi.org/10.1016/j.ipl.2018.07.009.

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Delgrande, J. P. "On Computing Belief Change Operations using Quantified Boolean Formulas." Journal of Logic and Computation 14, no. 6 (2004): 801–26. http://dx.doi.org/10.1093/logcom/14.6.801.

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Diller, Martin, Johannes Peter Wallner, and Stefan Woltran. "Reasoning in abstract dialectical frameworks using quantified Boolean formulas." Argument & Computation 6, no. 2 (2015): 149–77. http://dx.doi.org/10.1080/19462166.2015.1036922.

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Kleine Büning, Hans, and Xishun Zhao. "Computational complexity of quantified Boolean formulas with fixed maximal deficiency." Theoretical Computer Science 407, no. 1-3 (2008): 448–57. http://dx.doi.org/10.1016/j.tcs.2008.07.022.

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Pulina, Luca, and Armando Tacchella. "A self-adaptive multi-engine solver for quantified Boolean formulas." Constraints 14, no. 1 (2008): 80–116. http://dx.doi.org/10.1007/s10601-008-9051-2.

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Dissertations / Theses on the topic "Quantified Boolean Formulas"

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Skelley, Alan. "Relating the PSPACE reasoning power of Boolean Programs and quantified Boolean formulas." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ53391.pdf.

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Cashmore, Michael. "Planning as quantified Boolean formulae." Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=22387.

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This work explores the idea of classical Planning as Quantified Boolean Formulae. Planning as Satisfiability (SAT) is a popular approach to Planning and has been explored in detail producing many compact and efficient encodings, Planning-specific solver implementations and innovative new constraints. However, Planning as Quantified Boolean Formulae (QBF) has been relegated to conformant Planning approaches, with the exception of one encoding that has not yet been investigated in detail. QBF is a promising setting for Planning given that the problems have the same complexity. This work introduc
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Ghasemzadeh, Mohammad. "A new algorithm for the quantified satisfiability problem, based on zero-suppressed binary decision diagrams and memoization." Phd thesis, [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=978444213.

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Samulowitz, Horst Cornelius. "Solving Quantified Boolean Formulas." Thesis, 2008. http://hdl.handle.net/1807/11124.

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Abstract Solving Quantified Boolean Formulas Horst Samulowitz Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 Many real-world problems do not have a simple algorithmic solution and casting these problems as search problems is often not only the simplest way of casting them, but also the most efficient way of solving them. In this thesis we will present several techniques to advance search-based algorithms in the context of solving quantified boolean formulas (QBF). QBF enables complex realworld problems including planning, two-play
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Mangassarian, Hratch. "Pseudo-Boolean satisfiability and quantified Boolean formulas in CAD for VLSI." 2008. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=772091&T=F.

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Lin, Shuo-Ren, and 林碩紝. "Simplification of Quantified Boolean Formula Certificates." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/27458066247791628128.

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碩士<br>國立臺灣大學<br>電子工程學研究所<br>101<br>The evaluation and validation of Quantified Boolean Formulas (QBFs) are important subjects in conquering application problems belonging to the PSPACE-complete complexity class. Many applications in computer science can be compactly encoded in term of QBFs. In certain applications, QBF certificates play an important role in providing essential information for synthesis. There are primarily two types of certificates: the syntactic type in the form of Q-resolution/Q-consensus proofs and the semantic type in the form of Herbrand/Skolem functions. The latter is pa
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Balabanov, Valeriy, and 包偉力. "Unified Certification of Quantified Boolean Formula Evaluation." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/03382845537606246561.

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碩士<br>國立臺灣大學<br>電子工程學研究所<br>99<br>Quantified Boolean Formulas (QBFs) have been widely used in many practical applications, such as: model checking, automated planning, non-monotonic reasoning, scheduling, multi-agent scenarios, etc. Many effective state-of-the-art QBF tools have been developed. However only a few of the modern solvers can certify their answer. The importance of QBF certification is two-fold. First, it ensures the correctness of a solver. Second, perhaps more importantly, if the certificate is in an appropriate form (specifically, in Skolem/Herbrand functions), it enables certa
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Hsu, Tzu-Chien, and 徐子騫. "Reconstructing and Utilizing Circuit Information in Quantified Boolean Formula Solving." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/17722740081935749042.

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碩士<br>國立臺灣大學<br>電子工程學研究所<br>104<br>Quantified Boolean Satisfiability (QSAT) is a powerful representation since any problem in PSPACE can be encoded naturally and compactly as QSAT. Due to its representational power, QSAT is gaining increasing research attention, and many effective quantified Boolean formula(QBF) solvers have been developed recently, yet these solvers remain premature and awaits further breakthroughs for industrial applications. There are two main procedures used for QBF solver, including solution learning and conflict learning. One of the main researches about conjunctive norm
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Goultiaeva, Alexandra. "Exploiting Problem Structure in QBF Solving." Thesis, 2014. http://hdl.handle.net/1807/44111.

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Deciding the truth of a Quantified Boolean Formula (QBF) is a canonical PSPACE-complete problem. It provides a powerful framework for encoding problems that lie in PSPACE. These include many problems in automatic verification, and problems with discrete uncertainty or non-determinism. Two person adversarial games are another type of problem that are naturally encoded in QBF. It is standard practice to use Conjunctive Normal Form (CNF) when representing QBFs. Any propositional formula can be efficiently translated to CNF via the addition of new variables, and solvers can be implemented more ef
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Books on the topic "Quantified Boolean Formulas"

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Skelley, Alan. Relating the PSPACE reasoning power of boolean programs and quantified boolean formulas. National Library of Canada, 2000.

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Book chapters on the topic "Quantified Boolean Formulas"

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Kauers, Manuel, and Martina Seidl. "Symmetries of Quantified Boolean Formulas." In Theory and Applications of Satisfiability Testing – SAT 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94144-8_13.

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Flögel, Andreas, Marek Karpinski, and Hans Kleine Büning. "Subclasses of quantified boolean formulas." In Computer Science Logic. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54487-9_57.

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Büning, Hans Kleine, and Xishun Zhao. "Minimal False Quantified Boolean Formulas." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11814948_32.

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Kleine Büning, Hans, K. Subramani, and Xishun Zhao. "On Boolean Models for Quantified Boolean Horn Formulas." In Theory and Applications of Satisfiability Testing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24605-3_8.

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Lonsing, Florian, and Martina Seidl. "Parallel Solving of Quantified Boolean Formulas." In Handbook of Parallel Constraint Reasoning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63516-3_4.

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Büning, Hans Kleine, and Xishun Zhao. "On Models for Quantified Boolean Formulas." In Logic versus Approximation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25967-1_3.

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Büning, Hans Kleine, and Xishun Zhao. "Equivalence Models for Quantified Boolean Formulas." In Theory and Applications of Satisfiability Testing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527695_18.

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Bubeck, Uwe, and Hans Kleine Büning. "Nested Boolean Functions as Models for Quantified Boolean Formulas." In Theory and Applications of Satisfiability Testing – SAT 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39071-5_20.

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Fazekas, Katalin, Marijn J. H. Heule, Martina Seidl, and Armin Biere. "Skolem Function Continuation for Quantified Boolean Formulas." In Tests and Proofs. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61467-0_8.

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Janota, Mikoláš. "On Unordered BDDs and Quantified Boolean Formulas." In Progress in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30244-3_41.

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Conference papers on the topic "Quantified Boolean Formulas"

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Hristov, N., and A. Remshagen. "Local search for quantified Boolean formulas." In the 43rd annual southeast regional conference. ACM Press, 2005. http://dx.doi.org/10.1145/1167350.1167390.

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Seidl, Martina, and Robert Konighofer. "Partial witnesses from preprocessed quantified Boolean formulas." In Design Automation and Test in Europe. IEEE Conference Publications, 2014. http://dx.doi.org/10.7873/date.2014.162.

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Seidl, Martina, and Robert Konighofer. "Partial witnesses from preprocessed quantified Boolean formulas." In Design Automation and Test in Europe. IEEE Conference Publications, 2014. http://dx.doi.org/10.7873/date2014.162.

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Shukla, Ankit, Armin Biere, Luca Pulina, and Martina Seidl. "A Survey on Applications of Quantified Boolean Formulas." In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2019. http://dx.doi.org/10.1109/ictai.2019.00020.

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Dalmau, Víctor. "A dichotomy theorem for learning quantified Boolean formulas." In the tenth annual conference. ACM Press, 1997. http://dx.doi.org/10.1145/267460.267496.

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Fazekas, Katalin, Martina Seidl, and Armin Biere. "A Duality-Aware Calculus for Quantified Boolean Formulas." In 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2016. http://dx.doi.org/10.1109/synasc.2016.038.

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Lee, Nian-Ze, Yen-Shi Wang, and Jie-Hong R. Jiang. "Solving Exist-Random Quantified Stochastic Boolean Satisfiability via Clause Selection." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/186.

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Stochastic Boolean satisfiability (SSAT) is an expressive language to formulate decision problems with randomness. Solving SSAT formulas has the same PSPACE-complete computational complexity as solving quantified Boolean formulas (QBFs). Despite its broad applications and profound theoretical values, SSAT has received relatively little attention compared to QBF. In this paper, we focus on exist-random quantified SSAT formulas, also known as E-MAJSAT, which is a special fragment of SSAT commonly applied in probabilistic conformant planning, posteriori hypothesis, and maximum expected utility. B
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Yang, Jun-Cheng, Shu-Xia Li, and Jin-Yan Wang. "Computation of renameable horn backdoors for quantified boolean formulas." In 2010 2nd International Conference on Future Computer and Communication. IEEE, 2010. http://dx.doi.org/10.1109/icfcc.2010.5497649.

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Lee, Nian-Ze, Yen-Shi Wang, and Jie-Hong R. Jiang. "Solving Stochastic Boolean Satisfiability under Random-Exist Quantification." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/96.

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Stochastic Boolean Satisfiability (SSAT) is a powerful formalism to represent computational problems with uncertainly, such as belief network inference and propositional probabilistic planning. Solving SSAT formulas lies in the same complexity class (PSPACE-complete) as solving Quantified Boolean Formula (QBF). While many endeavors have been made to enhance QBF solving, SSAT has drawn relatively less attention in recent years. This paper focuses on random-exist quantified SSAT formulas, and proposes an algorithm combining binary decision diagram (BDD), logic synthesis, and modern SAT technique
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Santhanam, Rahul, and Ryan Williams. "Beating Exhaustive Search for Quantified Boolean Formulas and Connections to Circuit Complexity." In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973730.18.

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