CAQE and QuAbS: Abstraction Based QBF Solvers

Tentrup, Leander

doi:10.3233/sat190121

Cited by 8 publications

(5 citation statements)

References 55 publications

(103 reference statements)

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“…In the case of the SAT-based algorithm, we use Glucose 4 [24], a state-of-the-art SAT solver, via the Python module python-sat. 5 In the case of the QBF-based algorithm, we use the state-of-the-art CAQE [25,26] QBF solver, although any other QBF solver that accepts QDIMACS standard 6 input and output may be used.…”

Section: Experiments and Resultsmentioning

confidence: 99%

General Boolean Formula Minimization with QBF Solvers

Calò,

Levy

2023

Frontiers in Artificial Intelligence and Applications

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The minimization of propositional formulae is a classical problem in logic, whose first algorithms date back at least to the 1950s with the works of Quine and Karnaugh. Most previous work in the area has focused on obtaining minimal, or quasi-minimal, formulae in conjunctive normal form (CNF) or disjunctive normal form (DNF), with applications in hardware design. In this paper, we are interested in the problem of obtaining an equivalent formula in any format, also allowing connectives that are not present in the original formula. We are primarily motivated in applying minimization algorithms to generate natural language translations of the original formula, where using shorter equivalents as input may result in better translations. Buchfuhrer and Umans have proved that the (decisional version of the) problem is Σp2-complete. We analyze three possible (practical) approaches to solving the problem. First, using brute force, generating all possible formulae in increasing size and checking if they are equivalent to the original formula by testing all possible variable assignments. Second, generating the Tseitin coding of all the formulae and checking equivalence with the original using a SAT solver. Third, encoding the problem as a Quantified Boolean Formula (QBF), and using a QBF solver. Our results show that the QBF approach largely outperforms the other two.

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Section: Experiments and Resultsmentioning

confidence: 99%

General Boolean Formula Minimization with QBF Solvers

Calò,

Levy

2023

Frontiers in Artificial Intelligence and Applications

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“…We evaluated our algorithm in Section 3 on cases that require single or nested trajectories. The trajectory encoding presented in Section 3 is implemented on top of the open-source bounded model checker HyperQB [15], and the QBF solver QuABs [19]. All experiments are executed on a MacBook Pro with 2.2GHz processor and 16GB RAM 4 .…”

Section: Case Studies and Evaluationmentioning

confidence: 99%

Bounded Model Checking for Asynchronous Hyperproperties

Hsu¹,

Bonakdarpour²,

Finkbeiner³

et al. 2023

Preprint

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Many types of attacks on confidentiality stem from the nondeterministic nature of the environment that computer programs operate in (e.g., schedulers and asynchronous communication channels). In this paper, we focus on verification of confidentiality in nondeterministic environments by reasoning about asynchronous hyperproperties. First, we generalize the temporal logic A-HLTL to allow nested trajectory quantification, where a trajectory determines how different execution traces may advance and stutter. We propose a bounded model checking algorithm for A-HLTL based on QBF-solving for a fragment of the generalized A-HLTL and evaluate it by various case studies on concurrent programs, scheduling attacks, compiler optimization, speculative execution, and cache timing attacks. We also rigorously analyze the complexity of model checking for different fragments of A-HLTL. 19 p r i n t ( ' d ' ) ; 20 }

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“…The strength of the QBF-based algorithm is that it computes a scheme T n of all candidates ψ ′ i of length n, instead of checking each one of ψ ′ i for equivalence with ψ. Tseitin transformation (Tseitin, 1983) is used to encode the equivalence of T n and ψ as a QBF formula, which is checked for satisfiability (see Section 2) by a QBF solver (Tentrup, 2019). The algorithm can find all ψ ′ i ∈ P of a certain length n. By making several calls to the QBF solver, increasing n, we make sure that the first found solution is a minimal solution.…”

Section: Algorithmmentioning

confidence: 99%

Is Shortest Always Best? The Role of Brevity in Logic-to-Text Generation

Calò,

Levy,

Gatt

et al. 2023

Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

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Some applications of artificial intelligence make it desirable that logical formulae be converted computationally to comprehensible natural language sentences. As there are many logical equivalents to a given formula, finding the most suitable equivalent to be used as input for such a "logic-to-text" generation system is a difficult challenge. In this paper, we focus on the role of brevity: Are the shortest formulae the most suitable? We focus on propositional logic (PL), framing formula minimization (i.e., the problem of finding the shortest equivalent of a given formula) as a Quantified Boolean Formulae (QBFs) satisfiability problem. We experiment with several generators and selection strategies to prune the resulting candidates. We conduct exhaustive automatic and human evaluations of the comprehensibility and fluency of the generated texts. The results suggest that while, in many cases, minimization has a positive impact on the quality of the sentences generated, formula minimization may ultimately not be the best strategy.https://gitlab.nl4xai.eu/eduardo. calo/brevity-PL

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CAQE and QuAbS: Abstraction Based QBF Solvers

Cited by 8 publications

References 55 publications

General Boolean Formula Minimization with QBF Solvers

General Boolean Formula Minimization with QBF Solvers

Bounded Model Checking for Asynchronous Hyperproperties

Is Shortest Always Best? The Role of Brevity in Logic-to-Text Generation

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