Non-prenex QBF Solving Using Abstraction

Tentrup, Leander

doi:10.1007/978-3-319-40970-2_24

Cited by 30 publications

(23 citation statements)

References 13 publications

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“…Recent work successfully combines machine learning with this CEGAR approach [24]. Motivated by the success of expansion-based QBF solving, several other approaches [9], [29], [38], [41]- [43] have been presented that are based on levelised SAT solving, i.e., one SAT solver is responsible for the variables of one quantifier block. In this paper, we also introduce a solving approach that is based upon propositional abstraction but considers the whole quantifier prefix at once.…”

Section: Related Workmentioning

confidence: 99%

Expansion-Based QBF Solving Without Recursion

Bloem

Braud-Santoni

Hadzic

et al. 2018

2018 Formal Methods in Computer Aided Design (FMCAD)

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In recent years, expansion-based techniques have been shown to be very powerful in theory and practice for solving quantified Boolean formulas (QBF), the extension of propositional formulas with existential and universal quantifiers over Boolean variables. Such approaches partially expand one type of variable (either existential or universal) and pass the obtained formula to a SAT solver for deciding the QBF. State-of-the-art expansionbased solvers process the given formula quantifier-block wise and recursively apply expansion until a solution is found.In this paper, we present a novel algorithm for expansionbased QBF solving that deals with the whole quantifier prefix at once. Hence recursive applications of the expansion principle are avoided. Experiments indicate that the performance of our simple approach is comparable with the state of the art of QBF solving, especially in combination with other solving techniques.

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Section: Related Workmentioning

confidence: 99%

Expansion-Based QBF Solving Without Recursion

Bloem

Braud-Santoni

Hadzic

et al. 2018

2018 Formal Methods in Computer Aided Design (FMCAD)

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“…As we have a 2-QBF not in CNF, solvers using counterexamples to refine an abstraction (CEGAR-based) [3] showed the best performance. We therefore decided for the solver QUABS [17] as it combines fast parsing with fast solving. Given a bound on the length of the proof of correctness for a strategy, we further pruned the bounded unfolding of unreachable places and unreachable transitions to remove unnecessary variables from the 2-QBF, which increased the overall performance.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…This search is encoded in a quantified Boolean formula (QBF) and solved by a QBF solver. We report on our experience with a first prototype implementation of bounded synthesis generating the QBF and solving it with the QBF solver QUABS [17] in comparison to the symbolic approach implemented in ADAM.…”

Section: Introductionmentioning

confidence: 99%

Symbolic vs. Bounded Synthesis for Petri Games

Finkbeiner

Gieseking

Hecking-Harbusch

et al. 2017

Electron. Proc. Theor. Comput. Sci.

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Petri games are a multiplayer game model for the automatic synthesis of distributed systems. We compare two fundamentally different approaches for solving Petri games. The symbolic approach decides the existence of a winning strategy via a reduction to a two-player game over a finite graph, which in turn is solved by a fixed point iteration based on binary decision diagrams (BDDs). The bounded synthesis approach encodes the existence of a winning strategy, up to a given bound on the size of the strategy, as a quantified Boolean formula (QBF). In this paper, we report on initial experience with a prototype implementation of the bounded synthesis approach. We compare bounded synthesis to the existing implementation of the symbolic approach in the synthesis tool ADAM. We present experimental results on a collection of benchmarks, including one new benchmark family, modeling manufacturing and workflow scenarios with multiple concurrent processes.

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“…In this work, we take look at the closely related field of QBF solving. There pure CEGAR solving [11][12][13] on the CNF representation is not competitive anymore [14], and it has been augmented by preprocessing [15,16], circuit representations [17][18][19][20][21], and Incremental Determinization (ID) [22]. It may hence be fruitful to leverage some of the recent developments of QBF.…”

Section: Introductionmentioning

confidence: 99%

Incremental Determinization for Quantifier Elimination and Functional Synthesis

Rabe

2019

Computer Aided Verification

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Quantifier elimination and its cousin functional synthesis are fundamental problems in automated reasoning that could be used in many applications of formal methods. But, effective algorithms are still elusive. In this paper, we suggest a simple modification to a QBF algorithm to adapt it for quantifier elimination and functional synthesis. We demonstrate that the approach significantly outperforms previous algorithms for functional synthesis.

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Non-prenex QBF Solving Using Abstraction

Cited by 30 publications

References 13 publications

Expansion-Based QBF Solving Without Recursion

Expansion-Based QBF Solving Without Recursion

Symbolic vs. Bounded Synthesis for Petri Games

Incremental Determinization for Quantifier Elimination and Functional Synthesis

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