The Seventh QBF Solvers Evaluation (QBFEVAL’10)

Peschiera, Claudia; Pulina, Luca; Tacchella, Armando; Bubeck, Uwe; Kullmann, Oliver; Lynce, Inês

doi:10.1007/978-3-642-14186-7_20

Cited by 22 publications

(25 citation statements)

References 14 publications

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“…Additionally, unit clauses learnt by PicoSAT are propagated using QBF-specific QBCP rules within QxBF. Table 1 compares the impact of different FL approaches on the performance of QBF solvers based on search (DepQBF [17] and QuBE7.1 [10]) and variable elimination (Quantor [2], squolem [15] and Nenofex [16]) using all benchmarks from QBFEVAL'10 [20]. For QuBE7.1 internal preprocessing was disabled (QuBE7.1-np).…”

Section: Methodsmentioning

confidence: 99%

Failed Literal Detection for QBF

Lonsing

Biere

2011

Theory and Applications of Satisfiability Testing - SAT 2011

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Abstract. Failed literal detection (FL) in SAT is a powerful approach for preprocessing. The basic idea is to assign a variable as assumption. If boolean constraint propagation (BCP) yields an empty clause then the negated assumption is necessary for satisfiability. Whereas FL is common in SAT, it cannot easily be applied to QBF due to universal quantification. We present two approaches for FL to preprocess prenex CNFs. The first one is based on abstraction where certain universal variables are treated as existentially quantified. Second we combine QBF-specific BCP (QBCP) in FL with Q-resolution to validate assignments learnt by FL. Finally we compare these two approaches to a third common approach based on SAT. It turns out that the three approaches are incomparable. Experimental evaluation demonstrates that FL for QBF can improve the performance of search-and elimination-based QBF solvers.

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Section: Methodsmentioning

confidence: 99%

Failed Literal Detection for QBF

Lonsing

Biere

2011

Theory and Applications of Satisfiability Testing - SAT 2011

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“…In the proof of the following theorem, we actually show the upper bound by reduction to ∀∃-QBF instead of by an alternating procedure. This paves the way to using highly optimised QBF solvers [33] for deciding T ≡ Σ ∅.…”

Section: Concept Signatures and The Empty Tboxmentioning

confidence: 99%

Model-theoretic inseparability and modularity of description logic ontologies

Konev

Lutz

Walther

et al. 2013

Artificial Intelligence

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The aim of this paper is to introduce and study model-theoretic notions of modularity in description logic and related reasoning problems. Our approach is based on a generalisation of logical equivalence that is called modeltheoretic inseparability. Two TBoxes are inseparable w.r.t. a vocabulary Σ if they cannot be distinguished by the Σ-reducts of their models and thus can equivalently be replaced by one another in any application where only vocabulary items from Σ are relevant. We study in-depth the complexity of deciding inseparability for the description logics EL and ALC and their extensions with inverse roles. We then discuss notions of modules of a TBox based on model-theoretic inseparability and develop algorithms for extracting minimal modules from acyclic TBoxes. Finally, we provide an experimental evaluation of our module extraction algorithm based on the large-scale medical TBox Snomed ct.

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“…SPLAnE generated the random traceability relation between feature model and component model to generate a complete SPL model. Further, to increase the complexity of experiments, each SPL model is generated using 10 different topologies and 10 different level of cross-tree constraints with percentage as {5, 10,15,20,25,30,35,40,45, 50}, resulting in a total of 100 SPL models per SPLOT model. So from 698 SPLOT models, we got 69800 SPL models.…”

Section: Experiments 1: Validating Splane With Feature Models From Thementioning

confidence: 99%

“…For each SPL model, 10 different topologies were generated to avoid the threats to internal validity. Further, to increase the complexity of experiments, 10 different levels of cross-tree constraints {5, 10,15,20,25,30,35,40,45, 50} were added. Each randomly generated SPL models consists of a feature model, a component model and a traceability relation.…”

Section: Experiments 2: Validating Splane With Randomly Generated Largmentioning

confidence: 99%

Traceability Analyses between Features and Assets in Software Product Lines

Narwane

Galindo

Krishna

et al. 2016

Entropy

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Abstract:In a Software Product Line (SPL), the central notion of implementability provides the requisite connection between specifications and their implementations, leading to the definition of products. While it appears to be a simple extension of the traceability relation between components and features, it involves several subtle issues that were overlooked in the existing literature. In this paper, we have introduced a precise and formal definition of implementability over a fairly expressive traceability relation. The consequent definition of products in the given SPL naturally entails a set of useful analysis problems that are either refinements of known problems or are completely novel. We also propose a new approach to solve these analysis problems by encoding them as Quantified Boolean Formulae (QBF) and solving them through Quantified Satisfiability (QSAT) solvers. QBF can represent more complex analysis operations, which cannot be represented by using propositional formulae. The methodology scales much better than the SAT-based solutions hinted in the literature and were demonstrated through a tool called SPLAnE (SPL Analysis Engine) on a large set of SPL models.

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The Seventh QBF Solvers Evaluation (QBFEVAL’10)

Cited by 22 publications

References 14 publications

Failed Literal Detection for QBF

Failed Literal Detection for QBF

Model-theoretic inseparability and modularity of description logic ontologies

Traceability Analyses between Features and Assets in Software Product Lines

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