Abstract:Abstract-Conjunctive Normal Form (CNF) representation as used by most modern Quantified Boolean Formula (QBF) solvers is simple and powerful when reasoning about conflicts, but is not efficient at dealing with solutions. To overcome this inefficiency a number of specialized non-CNF solvers were created. These solvers were shown to have great advantages. Unfortunately, non-CNF solvers cannot benefit from sophisticated CNF-based techniques developed over the years. This paper demonstrates how the power of non-CN… Show more
“…These operations can be seen as an extension of traditional CDCL SAT solving [25,24] to QBF [30,12,10] and are discussed in more detail in turn.…”
Section: Propagation and Learning In Qbfmentioning
confidence: 99%
“…For this presentation we mainly rely on the approach of Goultiaeva et al [12], with some ingredients introduced by Klieber [19,18]. We note, however that the presented algorithm is not limited to this particular implementation of propagation.…”
Section: Propagatementioning
confidence: 99%
“…In one approach, a solver traverses the search space, while pruning it by propagation, similarly to SAT solvers [30,31,19,21,12,22]. In the second approach, a solver eliminates (expands) quantifiers obtaining thus a SAT problem [26,6,20,3].…”
Section: Related Workmentioning
confidence: 99%
“…Propagation can be realized in a number of different ways [30,31,19,21,12,22]. For this presentation we mainly rely on the approach of Goultiaeva et al [12], with some ingredients introduced by Klieber [19,18].…”
Section: Propagatementioning
confidence: 99%
“…It uses DPLL-style clause learning [30,12], abstraction & refinement learning of RAReQS [15,14], and the flat architecture of qesto [16].…”
Section: Combining Propagation and Refinementmentioning
QBF solving techniques can be divided into the DPLL-based and expansion-based. In this paper we look at how these two techniques can be combined while using strategy extraction as a means of interaction between the two. Once propagation derives a conflict for one of the players, we analyse the proof of such and devise a strategy for the opponent. Subsequently, this strategy is used to constrain the losing player. The implemented prototype shows feasibility of the approach. A number of avenues for future research can be envisioned. Can strategies be employed in a different fashion? Can better strategy be constructed? Do the presented techniques generalize beyond QBF?
“…These operations can be seen as an extension of traditional CDCL SAT solving [25,24] to QBF [30,12,10] and are discussed in more detail in turn.…”
Section: Propagation and Learning In Qbfmentioning
confidence: 99%
“…For this presentation we mainly rely on the approach of Goultiaeva et al [12], with some ingredients introduced by Klieber [19,18]. We note, however that the presented algorithm is not limited to this particular implementation of propagation.…”
Section: Propagatementioning
confidence: 99%
“…In one approach, a solver traverses the search space, while pruning it by propagation, similarly to SAT solvers [30,31,19,21,12,22]. In the second approach, a solver eliminates (expands) quantifiers obtaining thus a SAT problem [26,6,20,3].…”
Section: Related Workmentioning
confidence: 99%
“…Propagation can be realized in a number of different ways [30,31,19,21,12,22]. For this presentation we mainly rely on the approach of Goultiaeva et al [12], with some ingredients introduced by Klieber [19,18].…”
Section: Propagatementioning
confidence: 99%
“…It uses DPLL-style clause learning [30,12], abstraction & refinement learning of RAReQS [15,14], and the flat architecture of qesto [16].…”
Section: Combining Propagation and Refinementmentioning
QBF solving techniques can be divided into the DPLL-based and expansion-based. In this paper we look at how these two techniques can be combined while using strategy extraction as a means of interaction between the two. Once propagation derives a conflict for one of the players, we analyse the proof of such and devise a strategy for the opponent. Subsequently, this strategy is used to constrain the losing player. The implemented prototype shows feasibility of the approach. A number of avenues for future research can be envisioned. Can strategies be employed in a different fashion? Can better strategy be constructed? Do the presented techniques generalize beyond QBF?
Abstract. We consider the problem of incrementally solving a sequence of quantified Boolean formulae (QBF). Incremental solving aims at using information learned from one formula in the process of solving the next formulae in the sequence. Based on a general overview of the problem and related challenges, we present an approach to incremental QBF solving which is application-independent and hence applicable to QBF encodings of arbitrary problems. We implemented this approach in our incremental search-based QBF solver DepQBF and report on implementation details. Experimental results illustrate the potential benefits of incremental solving in QBF-based workflows.
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