不同的研究视角

 

 

1.学习子句生成器

Gilles Audemard proposed to renew the vision of CDCL solvers，instead of seeing them as an improvement of a DPLL search, seeing them as clauses producers.  Gilles Audemard 提议更新 CDCL 求解器的愿景，不是将它们视为 DPLL 搜索的改进，而是将它们视为子句生产者。 Gilles Audemard, Laurent Simon: On the Glucose SAT Solver. Int. J. Artif. Intell. Tools 27(1): 1840001:1-1840001:25 (2018)   @article{DBLP:journals/ijait/AudemardS18, author = {Gilles Audemard and Laurent Simon}, title = {On the Glucose {SAT} Solver}, journal = {Int. J. Artif. Intell. Tools}, volume = {27}, number = {1}, pages = {1840001:1--1840001:25}, year = {2018}, url = {https://doi.org/10.1142/S0218213018400018}, doi = {10.1142/S0218213018400018}, timestamp = {Tue, 12 May 2020 16:53:25 +0200}, biburl = {https://dblp.org/rec/journals/ijait/AudemardS18.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }

 

 

 

2.强化学习

	2019 Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro: Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning. CoRR abs/1909.11830 (2019) 2008 Roberto Battiti, Paolo Campigotto: Reinforcement Learning and Reactive Search: an adaptive MAX-SAT solver. ECAI 2008: 909-910
	Exponential Recency Weighted Average Branching Heuristic for SAT Solvers Inspired by the bandit framework and reinforcement learning, we learn to choose good variables to branch based on past experience. Our goal is to leverage the theory and practice of a rich sub-field of reinforcement learning to  plain and design an effective branching heuristic for solving real-world problems.  译文：受到bandit框架和强化学习的启发，我们学会根据过去的经验选择好的变量进行分支。我们的目标是利用理论和实践的丰富子领域的强化学习，以平原和设计一个有效的分支启发式解决实际问题。 分支决策变元的选择包含强化学习的思想
	Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning
	Adaptive Restart and CEGAR-Based Solver for Inverting Cryptographic Hash Functions   MapleCrypt has two key features, namely, a multi-armed bandit based adaptive restart (MABR) policy and a counterexample-guided abstraction refinement (CEGAR) technique.译文：将固定目标的哈希函数反演问题简化为布尔逻辑的可满足性问题，并使用MapleCrypt构造这些目标的前像。MapleCrypt有两个关键特性，即基于多武装强盗的自适应重启(MABR)策略和反例引导的抽象细化(CEGAR)技术  The MABR technique uses reinforcement learning to adaptively choose between different restart policies during the run of the solver.译文：MABR技术使用强化学习来在求解器运行过程中自适应地选择不同的重启策略。

 

 

 

 

3.结构探测的视角

 

 

4. 特殊子句有限传播——体现了什么原理？强化学习？桥接变元？简化？

 

Specific clauses (and their variants) that are known to be studied include \textit{Glue clauses} and \textit{Core clauses}, as well as \textit{Duplicate Learnt Clauses}. They are either judged to be of high quality, or they are speculated to carry important information. All of them have been experimentally proven to play an important role in improving the ability to solve. %已知被研究的特定的子句（及其变元）包括胶水子句和核心子句以及复制学习子句。 %子句尺寸最小的glue子句在早期文献加强的重视成为研究的热点。 %glue first \medskip \noindent\textbf{\textsf{Glue First.}} In conflict-directed clause learning (CDCL) SAT solving, a class of learning clauses known as glue clauses is highly valued and serves as the basis for various heuristics\cite{abs-1904-11106}. Experimentally that branching decisions with variables appearing in glue clauses, called glue variables, are more conflict efficient than with nonglue variables,and the frequency of learned clauses that are glue clauses can be used as a reliable indicator of solving efficiency for some instances\cite{Chowdhury0Y19}. %一类被称为glue clause的学习子句得到高度重视并成为各种启发式的基本依据。   %core first \medskip \noindent\textbf{\textsf{Core First.}} Core first unit propagation strategies are proposed in 2019 \cite{abs-1907-01192}. The core clauses, which is defined as one with literal block distance less than or equal to 7, are prioritized by organizations to participate in conflict generationis in BCP. %这是与保留高质量的子句长久保留不被删除的策略的技术路线是一脉相承。在文献中，学习子句被按照质量高低分配到Core、Iter2、Local中，不同的集合元素的生存期（保留期限）被区别对待。这里的质量标准通常是LBD或子句中文字数量。Core集合中的子句被永久保留;Iter2中的子句被继续评价考察决定是否流动到其它两个集合;Local中的子句被定期删除至少一半数量。 %复制子句 \medskip \noindent\textbf{\textsf{Duplicate Learnt Clauses.}} CDCL solver may end up computing (exponentially) many conflict clauses. Based on the efficiency of BCP and the saving of storage space, it is necessary to evaluate and delete some learning clauses in stages to maintain a relatively controllable collection of learning clauses. However, the Duplicate Learnt Clauses is a special type of clause that is repeated in the implementation of the dynamic deletion policy in the learning clause management\cite{Kochemazov0SK20}. Miraculously, in the process of solving most of the CNF instances, a solver may learn and remove the same clause multiple times. Extracting duplicate clauses acutely and storing them indefinitely can be beneficial for the CDCL solver performance. %复制学习子句是在学习子句管理的动态删除策略实施中长期观察发现的一类特殊子句类型。 %基于BCP效率和节省存储空间考虑，需要阶段性地评价和删除一些学习子句，维持一个相对可控规模的学习子句集合。 %在求解大多数样例的过程中 The above three types of clauses was given increased emphasis and was called important entry points for improving solver performance. So, the horn clause (and the literals it contains) is also studied as an entry point from the perspective of ordered linear reduction in the next chapter.