Incremental Compilation-to-SAT Procedures

Benedetti, Marco; Bernardini, Sara

doi:10.1007/11527695_4

Cited by 8 publications

(5 citation statements)

References 20 publications

(32 reference statements)

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“…As a promising learning based approach, incremental SAT [6][7][8][9] tries to leverage the similarity between the elements of a sequence of SAT instances-most do so by re-utilizing learned knowledge based on conflict clauses. When many closely related instances need to be solved, caching solutions [10] and incremental translation [11] can also be effective. If a SAT instance is obtained from another by augmenting some clauses as described in [12], all conflict clauses of the first can be forwarded to the second.…”

Section: Related Workmentioning

confidence: 99%

Property Clustering and Learning Techniques

Chen

Qin

Koo

et al. 2012

System-Level Validation

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Section: Related Workmentioning

confidence: 99%

Property Clustering and Learning Techniques

Chen

Qin

Koo

et al. 2012

System-Level Validation

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“…Jin and Somenzi [JS05] present efficient ways of filtering learned clauses when creating the next problem instance. In [BB04] a framework for incremental SAT solving based on incremental compilation of the encoding to SAT is presented, however, their PLTL encoding is based on the original and inefficient (for past formulas) encoding of [BC03].…”

Section: Incremental Sat and Bmcmentioning

confidence: 99%

Linear Encodings of Bounded LTL Model Checking

Biere

Heljanko²,

Junttila³

et al. 2006

Logical Methods in Computer Science

145

188

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Abstract. We consider the problem of bounded model checking (BMC) for linear temporal logic (LTL). We present several efficient encodings that have size linear in the bound. Furthermore, we show how the encodings can be extended to LTL with past operators (PLTL). The generalised encoding is still of linear size, but cannot detect minimal length counterexamples. By using the virtual unrolling technique minimal length counterexamples can be captured, however, the size of the encoding is quadratic in the specification. We also extend virtual unrolling to Büchi automata, enabling them to accept minimal length counterexamples.Our BMC encodings can be made incremental in order to benefit from incremental SAT technology. With fairly small modifications the incremental encoding can be further enhanced with a termination check, allowing us to prove properties with BMC.An analysis of the liveness-to-safety transformation reveals many similarities to the BMC encodings in this paper. We conduct experiments to determine the advantage of employing dedicated BMC encodings for PLTL over combining more general but potentially less efficient approaches with BMC: the liveness-to-safety transformation with invariant checking and Büchi automata with fair cycle detection.Experiments clearly show that our new encodings improve performance of BMC considerably, particularly in the case of the incremental encoding, and that they are very competitive for finding bugs. Dedicated encodings seem to have an advantage over using more general methods with BMC. Using the liveness-to-safety translation with BDD-based invariant checking results in an efficient method to find shortest counterexamples that complements the BMC-based approach. For proving complex properties BDD-based methods still tend to perform better.

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“…Also, algorithms seldom exploit the fact that real-world problems of the same type are solved again and again on a regular basis, maintaining the same combinatorial structure, but differing in the data. Problems of this type include, for example, SAT encodings of AI Planning instances (Robinson et al 2008) and Bounded Model Checking instances (Benedetti and Bernardini 2005).…”

Section: Introductionmentioning

confidence: 99%

Learning Interpretable Heuristics for WalkSAT

Interian

Bernardini

2023

Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning

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Local search algorithms are well-known methods for solving large, hard instances of the satisfiability problem (SAT). The performance of these algorithms crucially depends on heuristics for setting noise parameters and scoring variables. The optimal setting for these heuristics varies for different instance distributions. In this paper, we present an approach for learning effective variable scoring functions and noise parameters by using reinforcement learning. We consider satisfiability problems from different instance distributions and learn specialized heuristics for each of them. Our experimental results show improvements with respect to both a WalkSAT baseline and another local search learned heuristic.

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Incremental Compilation-to-SAT Procedures

Cited by 8 publications

References 20 publications

Property Clustering and Learning Techniques

Property Clustering and Learning Techniques

Linear Encodings of Bounded LTL Model Checking

Learning Interpretable Heuristics for WalkSAT

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