Report of the Third QBF Solvers Evaluation1

Narizzano, Massimo; Pulina, Luca; Tacchella, Armando

doi:10.3233/sat190019

Cited by 21 publications

(16 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those experiments show that the current solvers do not have problems for solving instances from QCI and QOCNF < . This is in contrast with our previous experiments on some other tractable (but incomplete) fragments for the validity problem (quantified Horn CNF and quantified renamable Horn CNF) [48] that were confirmed during the latest QBF evaluation [29].…”

Section: Resultscontrasting

confidence: 75%

“…This can be explained by the fact that, as the canonical PSPACE-complete problem, many AI problems can be polynomially reduced to val(QPROP P S ) (see e.g., [14,15,16,17,18]); in particular, val(QPROP P S ) includes sat as a specific case; furthermore, there is some empirical evidence from various AI fields (including among others planning, nonmonotonic reasoning, paraconsistent inference) that a translation-based approach can prove more "efficient" than domain-dependent algorithms dedicated to such AI tasks. Accordingly, many solvers for val(QPROP P S ) have been designed and evaluated for the past few years (see among others [19,20,21,22,23,24,25,26,27,28,29]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Complexity Results for Quantified Boolean Formulae Based on Complete Propositional Languages1

Coste‐Marquis

Berre

Letombe

et al. 2006

SAT

View full text Add to dashboard Cite

Several propositional fragments have been considered so far as target languages for knowledge compilation and used for improving computational tasks from major AI areas (like inference, diagnosis and planning); among them are the ordered binary decision diagrams, prime implicates, prime implicants, "formulae" in decomposable negation normal form. On the other hand, the validity problem val(QPROP P S) for Quantified Boolean Formulae (QBF) has been acknowledged for the past few years as an important issue for AI, and many solvers have been designed. In this paper, the complexity of restrictions of the validity problem for QBF obtained by imposing the matrix of the input QBF to belong to propositional fragments used as target languages for compilation, is identified. It turns out that this problem remains hard (PSPACE-complete) even under severe restrictions on the matrix of the input. Nevertheless some tractable restrictions are pointed out.

show abstract

Section: Resultscontrasting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Complexity Results for Quantified Boolean Formulae Based on Complete Propositional Languages1

Coste‐Marquis

Berre

Letombe

et al. 2006

SAT

View full text Add to dashboard Cite

show abstract

“…Indeed, no QBF/ASP solvers have ever been designated (or known) as specialized to random instances in ASP and QBF Evaluations so far (cf. [11,35] and http://www.qbflib.org). Nonetheless our models has other interesting implications for QBF and ASP solvers.…”

Section: Impact On Qbf and Asp Solvingmentioning

confidence: 99%

New models for generating hard random boolean formulas and disjunctive logic programs

Amendola

Ricca

Truszczyński

2020

Artificial Intelligence

View full text Add to dashboard Cite

We propose two models of random quantified boolean formulas and their natural random disjunctive logic program counterparts. The models extend the standard models of random k-CNF formulas and the Chen-Interian model of random 2QBFs. The first model controls the generation of programs and QSAT formulas by imposing a specific structure on rules and clauses, respectively. The second model is based on a family of QSAT formulas in a non-clausal form. We provide theoretical bounds for the phase transition region in our models, and show experimentally the presence of the easy-hard-easy pattern and its alignment with the location of the phase transition. We show that boolean formulas and logic programs from our models are significantly harder than those obtained from the standard k-CNF and Chen-Interian models, and that their combination yields formulas and programs that are "super-hard" to evaluate. We also provide evidence suggesting that formulas from one of our models are well suited for assessing solvers tuned to real-world instances. Finally, it is noteworthy that, to the best of our knowledge, our models and results on random disjunctive logic programs are the first of their kind. * Some of the results were presented in preliminary form at IJCAI 2017 [4].

show abstract

“…Most of these benchmarks are quite challenging for modern solvers. Some of them (e.g., the "adder" series) have never been completely solved [51,61].…”

Section: Some Publicly-available Qbf Instances From Fv Domainsmentioning

confidence: 99%

“…Conversely, they are rather difficult for SAT-based bounded MC techniques, as they capture the worst-case scenario in which the number of steps necessary to falsify the property equals the diameter of the system. The resulting instances are hard for current QBF solvers [51,61].…”

Section: Some Publicly-available Qbf Instances From Fv Domainsmentioning

confidence: 99%

QBF-Based Formal Verification: Experience and Perspectives

Benedetti

Mangassarian

2008

SAT

View full text Add to dashboard Cite

The language of Quantified Boolean Formulas (QBF) has a lot of potential applications to Formal Verification (FV) tasks, as it captures many of these tasks in a natural and compact way. Practical experience has been disappointing though. When compared with contending approaches such as SAT, QBF-based FV has invariably yielded unfavorable experimental results. This paper makes two contributions. We first provide an account of the status quo in QBF-based FV. We examine commonly adopted formalizations and the relative strengths of different decision procedures. In the second part of this paper, we investigate for the first time the relevance of some advanced QBF techniques to FV tasks. In particular, we describe the use and the benefits of restricted quantifiers, QBF certificates, alternative encodings for classical model checking problems, and encodings with free variables. These promising research perspectives seem to reverse the negative standing of QBF applied to FV, as confirmed by the experimental evidence we discuss. Experiments are conducted by extending the publicly available solver sKizzo in several ways, and they include the first case studies where QBF compares favorably to SAT, its traditional competitor. QBF turns out to be an order of magnitude faster than SAT in some tasks (e.g., automated design debugging of large circuits). Moreover, as the size of the problems grows, the SAT encodings result in excessive memory requirements leading to out-of-memory conditions, while the more compact QBF encodings continue to be manageable and solvable.

show abstract

Report of the Third QBF Solvers Evaluation1

Cited by 21 publications

References 22 publications

Complexity Results for Quantified Boolean Formulae Based on Complete Propositional Languages1

Complexity Results for Quantified Boolean Formulae Based on Complete Propositional Languages1

New models for generating hard random boolean formulas and disjunctive logic programs

QBF-Based Formal Verification: Experience and Perspectives

Contact Info

Product

Resources

About