As a complementary technology to Binary Decision Diagram-based(BDD-based) symbolic model checking, the verification techniques on Boolean satisfiability problem have gained an increasing wide of applications over the ...As a complementary technology to Binary Decision Diagram-based(BDD-based) symbolic model checking, the verification techniques on Boolean satisfiability problem have gained an increasing wide of applications over the last few decades, which brings a dramatic improvement for automatic verification. In this paper, we firstly introduce the theory about the Boolean satisfiability verification, including the description on the problem of Boolean satisfiability verification, Davis-Putnam-Logemann-Loveland(DPLL) based complete verification algorithm, and all kinds of solvers generated and the logic languages used by those solvers. Moreover, we formulate a large number optimizations of technique revolutions based on Boolean SATisfiability(SAT) and Satisfiability Modulo Theories(SMT) solving in detail, including incomplete methods such as bounded model checking, and other methods for concurrent programs model checking. Finally, we point out the major challenge pervasively in industrial practice and prospect directions for future research in the field of formal verification.展开更多
当前基于神经网络的端到端SAT求解模型在各类SAT问题求解上展现了巨大潜力。然而SAT问题难以容忍误差存在,神经网络模型无法保证不产生预测误差。为利用SAT问题实例特性来减少模型预测误差,提出了错误偏好变量嵌入架构(architecture of ...当前基于神经网络的端到端SAT求解模型在各类SAT问题求解上展现了巨大潜力。然而SAT问题难以容忍误差存在,神经网络模型无法保证不产生预测误差。为利用SAT问题实例特性来减少模型预测误差,提出了错误偏好变量嵌入架构(architecture of embedding error-preference variables, AEEV)。该架构包含错误偏好变量嵌入调整算法和动态部分标签训练模式。首先,为利用参与越多未满足子句的变量越可能被错误分类这一特性,提出了错误偏好变量嵌入调整算法,在消息传递过程中根据变量参与的未满足子句个数来调整其嵌入。此外,提出了动态部分标签监督训练模式,该模式利用了SAT问题实例的变量赋值之间存在复杂依赖关系这一特性,避免为全部变量提供标签,仅为错误偏好变量提供一组来自真实解的标签,保持其他变量标签为预测值不变,以在训练过程管理一个更小的搜索空间。最后,在3-SAT、k-SAT、k-Coloring、3-Clique、SHA-1原像攻击以及收集的SAT竞赛数据集上进行了实验验证。结果表明,相较于目前较先进的基于神经网络的端到端求解模型QuerySAT,AEEV在包含600个变量的k-SAT数据集上准确率提升了45.81%。展开更多
This paper reviews the recent literature on solving the Boolean satisfiability problem(SAT),an archetypal N P-complete problem,with the aid of machine learning(ML)techniques.Over the last decade,the machine learning s...This paper reviews the recent literature on solving the Boolean satisfiability problem(SAT),an archetypal N P-complete problem,with the aid of machine learning(ML)techniques.Over the last decade,the machine learning society advances rapidly and surpasses human performance on several tasks.This trend also inspires a number of works that apply machine learning methods for SAT solving.In this survey,we examine the evolving ML SAT solvers from naive classifiers with handcrafted features to emerging end-to-end SAT solvers,as well as recent progress on combinations of existing conflict-driven clause learning(CDCL)and local search solvers with machine learning methods.Overall,solving SAT with machine learning is a promising yet challenging research topic.We conclude the limitations of current works and suggest possible future directions.The collected paper list is available at https://github.com/ThinklabSJTU/awesome-ml4co.Keywords:Machine learning(ML),Boolean satisfiability(SAT),deep learning,graph neural networks(GNNs),combinatorial optimization.展开更多
Boolean satisfiability (SAT) is widely used as a solver engine in electronic design automation (EDA). Typically, SAT is used to determine whether one or more groups of variables can be combined to form a true formula....Boolean satisfiability (SAT) is widely used as a solver engine in electronic design automation (EDA). Typically, SAT is used to determine whether one or more groups of variables can be combined to form a true formula. All solutions SAT (AllSAT) is a variant of the SAT problem. In the fields of formal verification and pattern generation, AllSAT is particularly useful because it efficiently enumerates all possible solutions. In this paper, a semi-tensor product (STP) based AllSAT solver is proposed. The solver can solve instances described in both the conjunctive normal form (CNF) and circuit form. The implementation of our method differs from incremental enumeration because we do not add blocking conditions for existing solutions, but rather compute the matrices to obtain all the solutions in one pass. Additionally, the logical matrices support a variety of logic operations. Results from experiments with MCNC benchmarks using CNF-based and circuit-based forms show that our method can accelerate CPU time by 8.1x (238x maximum) and 19.9x (72x maximum), respectively.展开更多
提出了一种将布尔公式划分为子句组来进行布尔可满足性判定的方法.CNF(conjunctive normal form)公式是可满足的当且仅当划分产生的每个子句组都是可满足的,因此,通过判定子句组的可满足性来判定原公式的可满足性,相当于用分治法将复杂...提出了一种将布尔公式划分为子句组来进行布尔可满足性判定的方法.CNF(conjunctive normal form)公式是可满足的当且仅当划分产生的每个子句组都是可满足的,因此,通过判定子句组的可满足性来判定原公式的可满足性,相当于用分治法将复杂问题分解为多个子问题来求解.这种分治判定方法一方面降低了原公式的可满足性判定复杂度;另一方面,由于子句组的判定可以并行,因而判定速度能够得到进一步的提高.对于不能直接产生布尔子句组划分的情形,提出了一种利用聚类技术将CNF公式聚类成多个簇,然后消去簇间的公共变量来产生子句组划分的方法.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.61063002,61100186,61262008)Guangxi Natural Science Foundation of China(2011GXNSFA018164,2011GXNSFA018166,2012GXNSFAA053220)the Key Project of Education Department of Guangxi
文摘As a complementary technology to Binary Decision Diagram-based(BDD-based) symbolic model checking, the verification techniques on Boolean satisfiability problem have gained an increasing wide of applications over the last few decades, which brings a dramatic improvement for automatic verification. In this paper, we firstly introduce the theory about the Boolean satisfiability verification, including the description on the problem of Boolean satisfiability verification, Davis-Putnam-Logemann-Loveland(DPLL) based complete verification algorithm, and all kinds of solvers generated and the logic languages used by those solvers. Moreover, we formulate a large number optimizations of technique revolutions based on Boolean SATisfiability(SAT) and Satisfiability Modulo Theories(SMT) solving in detail, including incomplete methods such as bounded model checking, and other methods for concurrent programs model checking. Finally, we point out the major challenge pervasively in industrial practice and prospect directions for future research in the field of formal verification.
基金supported by National Key Research and Development Program of China(No.2020AAA0107600)National Science Foundation of China(No.62102258)+2 种基金Shanghai Pujiang Program,China(No.21PJ1407300)Shanghai Municipal Science and Technology Major Project,China(No.2021SHZDZX0102)Science and Technology Commission of Shanghai Municipality Project,China(No.22511105100),and also sponsored by Huawei Ltd,China.
文摘This paper reviews the recent literature on solving the Boolean satisfiability problem(SAT),an archetypal N P-complete problem,with the aid of machine learning(ML)techniques.Over the last decade,the machine learning society advances rapidly and surpasses human performance on several tasks.This trend also inspires a number of works that apply machine learning methods for SAT solving.In this survey,we examine the evolving ML SAT solvers from naive classifiers with handcrafted features to emerging end-to-end SAT solvers,as well as recent progress on combinations of existing conflict-driven clause learning(CDCL)and local search solvers with machine learning methods.Overall,solving SAT with machine learning is a promising yet challenging research topic.We conclude the limitations of current works and suggest possible future directions.The collected paper list is available at https://github.com/ThinklabSJTU/awesome-ml4co.Keywords:Machine learning(ML),Boolean satisfiability(SAT),deep learning,graph neural networks(GNNs),combinatorial optimization.
基金supported in part by the National Natural Science Foundation of China under Grant No.61871242in part by the State Key Laboratory of ASIC(Application Specific Integrated Circuit)&System of China under Grant No.2021KF008.
文摘Boolean satisfiability (SAT) is widely used as a solver engine in electronic design automation (EDA). Typically, SAT is used to determine whether one or more groups of variables can be combined to form a true formula. All solutions SAT (AllSAT) is a variant of the SAT problem. In the fields of formal verification and pattern generation, AllSAT is particularly useful because it efficiently enumerates all possible solutions. In this paper, a semi-tensor product (STP) based AllSAT solver is proposed. The solver can solve instances described in both the conjunctive normal form (CNF) and circuit form. The implementation of our method differs from incremental enumeration because we do not add blocking conditions for existing solutions, but rather compute the matrices to obtain all the solutions in one pass. Additionally, the logical matrices support a variety of logic operations. Results from experiments with MCNC benchmarks using CNF-based and circuit-based forms show that our method can accelerate CPU time by 8.1x (238x maximum) and 19.9x (72x maximum), respectively.
文摘提出了一种将布尔公式划分为子句组来进行布尔可满足性判定的方法.CNF(conjunctive normal form)公式是可满足的当且仅当划分产生的每个子句组都是可满足的,因此,通过判定子句组的可满足性来判定原公式的可满足性,相当于用分治法将复杂问题分解为多个子问题来求解.这种分治判定方法一方面降低了原公式的可满足性判定复杂度;另一方面,由于子句组的判定可以并行,因而判定速度能够得到进一步的提高.对于不能直接产生布尔子句组划分的情形,提出了一种利用聚类技术将CNF公式聚类成多个簇,然后消去簇间的公共变量来产生子句组划分的方法.
