QBF Gallery 2014: The QBF Competition at the FLoC 2014 Olympic Games

Janota, Mikoláš; Jordan, Charles; Klieber, Will; Lonsing, Florian; Seidl, Martina; Gelder, Allen Van

doi:10.3233/sat190108

Cited by 7 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the empirical evaluation, we used the original benchmark sets from the QBF Gallery 2014 [15] 8 preprocessing track (243 instances), QBFLIB track (276 instances), and applications track (735 instances). We compare the variants of DepQBF to RAReQS [18] and GhostQ [20], which showed top performance in the QBF Gallery 2014, and to the recent solvers CAQE [25] 9 , QESTO [17], and QELL [31].…”

Section: Resultsmentioning

confidence: 99%

“…Apart from QCDCL, orthogonal approaches to QBF solving have been developed. QBF competitions like the QBF Galleries 2013 [24] and 2014 [15] revealed the power of expansion-based approaches [1,6,18], which are based on a different proof system than search-based solving with QCDCL. We refer to related work [4,5] for an overview of QBF proof systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Q-Resolution with Generalized Axioms

Lonsing

Egly

Seidl³

2016

Theory and Applications of Satisfiability Testing – SAT 2016

Self Cite

View full text Add to dashboard Cite

Abstract. Q-resolution is a proof system for quantified Boolean formulas (QBFs) in prenex conjunctive normal form (PCNF) which underlies search-based QBF solvers with clause and cube learning (QCDCL). With the aim to derive and learn stronger clauses and cubes earlier in the search, we generalize the axioms of the Q-resolution calculus resulting in an exponentially more powerful proof system. The generalized axioms introduce an interface of Q-resolution to any other QBF proof system allowing for the direct combination of orthogonal solving techniques. We implemented a variant of the Q-resolution calculus with generalized axioms in the QBF solver DepQBF. As two case studies, we apply integrated SAT solving and resource-bounded QBF preprocessing during the search to heuristically detect potential axiom applications. Experiments with application benchmarks indicate a substantial performance improvement.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Q-Resolution with Generalized Axioms

Lonsing

Egly

Seidl³

2016

Theory and Applications of Satisfiability Testing – SAT 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…The second group of benchmarks is from recent papers on QBF applications: Reduction Finding [29] (also used in QBFGallery 2014 [25]), Circuit Understanding [18], Partial Equivalence [17], Reactive Synthesis [12]. We included the second group of benchmarks to also present cases in which incremental determinization is not superior to the existing approaches.…”

Section: Implementation and Experimental Evaluationmentioning

confidence: 99%

“…The incremental determinization algorithm encapsulates the reasoning about the assignments to the universal variables in the global conflict check, which is a propositional formula and can thus be offloaded to SAT solvers. The CEGAR approach for QBF [26][27][28]41] was most competitive in recent evaluations [19,25]. The basic idea is to maintain one SAT solver for each quantifier level.…”

Section: Related Workmentioning

confidence: 99%

Incremental Determinization

Rabe

Seshia

2016

Theory and Applications of Satisfiability Testing – SAT 2016

View full text Add to dashboard Cite

We present a novel approach to solve quantified boolean formulas with one quantifier alternation (2QBF). The algorithm incrementally adds new constraints to the formula until the constraints describe a unique Skolem function -or until the absence of a Skolem function is detected. Backtracking is required if the absence of Skolem functions depends on the newly introduced constraints. We present the algorithm in analogy to search algorithms for SAT and explain how propagation, decisions, and conflicts are lifted from values to Skolem functions. The algorithm improves over the state of the art in terms of the number of solved instances, solving time, and the size of the certificates.

show abstract

“…Efficient solvers are highly requested to solve QBF encodings of problems. Competitions like QBFEVAL or the QBF Galleries have been driving solver development [23,29,38]. State-of-the-art solvers are based on solving paradigms like, e.g., expansion [2,10,30] or Q-resolution [34].…”

Section: Introductionmentioning

confidence: 99%

Evaluating QBF Solvers: Quantifier Alternations Matter

Lonsing

Egly

2018

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

We present an experimental study of the effects of quantifier alternations on the evaluation of quantified Boolean formula (QBF) solvers. The number of quantifier alternations in a QBF in prenex conjunctive normal form (PCNF) is directly related to the theoretical hardness of the respective QBF satisfiability problem in the polynomial hierarchy. We show empirically that the performance of solvers based on different solving paradigms substantially varies depending on the numbers of alternations in PCNFs. In related theoretical work, quantifier alternations have become the focus of understanding the strengths and weaknesses of various QBF proof systems implemented in solvers. Our results motivate the development of methods to evaluate orthogonal solving paradigms by taking quantifier alternations into account. This is necessary to showcase the broad range of existing QBF solving paradigms for practical QBF applications. Moreover, we highlight the potential of combining different approaches and QBF proof systems in solvers.

show abstract

QBF Gallery 2014: The QBF Competition at the FLoC 2014 Olympic Games

Cited by 7 publications

References 35 publications

Q-Resolution with Generalized Axioms

Q-Resolution with Generalized Axioms

Incremental Determinization

Evaluating QBF Solvers: Quantifier Alternations Matter

Contact Info

Product

Resources

About