Enhancing Reasoning with the Extension Rule in CDCL SAT Solvers

The extension rule first introduced by G. Tseitin is a simple but powerful rule that, when added to resolution, leads to an exponentially stronger proof system known as extended resolution (ER). Despite the outstanding theoretical results obtained with ER, its exploitation in practice to improve SAT solvers' efficiency still poses some challenging issues. There have been several attempts in the literature aiming at integrating the extension rule within CDCL SAT solvers but the results are in general not as promising as in theory. An important remark that can be made on these attempts is that most of them focus on reducing the sizes of the proofs using the extended variables introduced in the solver. We adopt in this work a different view. We see extended variables as a means to enhance reasoning in solvers and therefore to give them the ability of reasoning on various semantic aspects of variables. Experiments carried out on the 2018 and 2020 SAT competitions' benchmarks show the use of the extension rule in CDCL SAT solvers to be practically beneficial for both satisfiable and unsatisfiable instances. La règle d'extension introduite pour la première fois par G. Tseitin est une règle simple mais puissante qui, ajoutée à la résolution, conduit à un système de preuves plus puissant appelé résolution étendue (ER). Malgré les résultats théoriques remarquables obtenus avec ER, son exploitation pratique pour améliorer l'efficacité des solveurs SAT pose encore quelques problèmes. Plusieurs tentatives visant à intégrer la règle d'extension aux solveurs CDCL SAT existent dans la littérature, mais les résultats ne sont en général pas aussi prometteurs qu'en théorie. Une remarque importante à faire sur ces tentatives est qu'elles se concentrent pour la plupart sur la réduction de la taille des preuves à l'aide des variables étendues introduites dans le solveur. Nous adoptons dans ce travail un point de vue différent. Nous considérons les variables étendues comme un moyen d'améliorer le raisonnement dans les solveurs et donc de leur donner la capacité de raisonner sur différents aspects sémantiques des variables. Les expérimentations réalisées sur les instances tirées des compétition SAT 2018 et 2020 montrent que l'utilisation de la règle d'extension dans les solveurs CDCL est bénéfique aussi bien pour les instances satisfiables que celles insatisfiables.

Study of Fine-grained Nested Parallelism in CDCL SAT Solvers

Boolean satisfiability (SAT) is an important performance-hungry problem with applications in many problem domains. However, most work on parallelizing SAT solvers has focused on coarse-grained, mostly embarrassing, parallelism. Here, we study fine-grained parallelism that can speed up existing sequential SAT solvers, which all happen to be of the so-called Conflict-Directed Clause Learning variety. We show the potential for speedups of up to 382× across a variety of problem instances. We hope that these results will stimulate future research, particularly with respect to a computer architecture open problem we present.

Two SAT solvers for solving quantified Boolean formulas with an arbitrary number of quantifier alternations

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) for obtaining a propositional abstraction of the QBF. If this formula is false, the truth value of the QBF is decided, otherwise further refinement steps are necessary. Classically, expansion-based solvers process the given formula quantifier-block wise and use one SAT solver per quantifier block. In this paper, we present a novel algorithm for expansion-based QBF solving that deals with the whole quantifier prefix at once. Hence recursive applications of the expansion principle are avoided and only two incremental SAT solvers are required. While our algorithm is naturally based on the $$\forall $$ ∀ Exp+Res calculus that is the formal foundation of expansion-based solving, it is conceptually simpler than present recursive approaches. 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.

Reducing SAT to Max2SAT

In the literature, we find reductions from 3SAT to Max2SAT. These reductions are based on the usage of a gadget, i.e., a combinatorial structure that allows translating constraints of one problem to constraints of another. Unfortunately, the generation of these gadgets lacks an intuitive or efficient method. In this paper, we provide an efficient and constructive method for Reducing SAT to Max2SAT and show empirical results of how MaxSAT solvers are more efficient than SAT solvers solving the translation of hard formulas for Resolution.

Model abstraction for discrete-event systems by binary linear programming with applications to manufacturing systems

Model abstraction for finite state automata is helpful for decreasing computational complexity and improving comprehensibility for the verification and control synthesis of discrete-event systems (DES). Supremal quasi-congruence equivalence is an effective method for reducing the state space of DES and its effective algorithms based on graph theory have been developed. In this paper, a new method is proposed to convert the supremal quasi-congruence computation into a binary linear programming problem which can be solved by many powerful integer linear programming and satisfiability (SAT) solvers. Partitioning states to cosets is considered as allocating states to an unknown number of cosets and the requirement of finding the coarsest quasi-congruence is equivalent to using the least number of cosets. The novelty of this paper is to solve the optimal partitioning problem as an optimal state-to-coset allocation problem. The task of finding the coarsest quasi-congruence is equivalent to the objective of finding the least number of cosets. Then the problem can be solved by optimization methods, which are respectively implemented by mixed integer linear programming (MILP) in MATLAB and binary linear programming (BLP) in CPLEX. To reduce the computation time, the translation process is first optimized by introducing fewer decision variables and simplifying constraints in the programming problem. Second, the translation process formulates a few techniques of converting logic constraints on finite automata into binary linear constraints. These techniques will be helpful for other researchers exploiting integer linear programming and SAT solvers for solving partitioning or grouping problems. Third, the computational efficiency and correctness of the proposed method are verified by two different solvers. The proposed model abstraction approach is applied to simplify the large-scale supervisor model of a manufacturing system with five automated guided vehicles. The proposed method is not only a new solution for the coarsest quasi-congruence computation, but also provides us a more intuitive understanding of the quasi-congruence relation in the supervisory control theory. A future research direction is to apply more computationally efficient solvers to compute the optimal state-to-coset allocation problem.

Analysis of comparative effectiveness of state-of-the-art heuristics for CDCL SAT solvers

The Conflict-Driven Clause Learning algorithms for solving the Boolean satisfiability problem comprise the major part of the methods used to solve various instances of the problems that arise in industry and science. In recent years there have been proposed several major heuristics for these algorithms which are assumed to be de facto good for the solvers’ performance over diverse sets of benchmarks. The goal of this paper is to evaluate the contribution of each separate heuristic to the performance of a state-of-the-art solver, see the extent to which they are beneficial, and figure out if the heuristics have any particular features that need to be taken into account.

Minimal Cardinality Diagnosis in Problems with Multiple Observations

Model-Based Diagnosis (MBD) is a well-known approach to diagnosis in medical domains. In this approach, the behavior of a system is modeled and used to identify faulty components, i.e., once a symptom of abnormal behavior is observed, an inference algorithm is run on the system model and returns possible explanations. Such explanations are referred to as diagnoses. A diagnosis is an assumption about which set of components are faulty and have caused the abnormal behavior. In this work, we focus on the case where multiple observations are available to the diagnoser, collected at different times, such that some of these observations exhibit symptoms of abnormal behavior. MBD with multiple observations is challenging because some components may fail intermittently, i.e., behave abnormally in one observation and behave normally in another, while other components may fail all the time (non-intermittently). Inspired by recent success in solving classical diagnosis problems using Boolean satisfiability (SAT) solvers, we describe two SAT-based approaches to solve this MBD with multiple observations problem. The first approach compiles the problem to a single SAT formula, and the second approach solves each observation independently and then merges them together. We compare these two approaches experimentally on a standard diagnosis benchmark and analyze their pros and cons.

Chapter 3. Complete Algorithms

Complete SAT algorithms form an important part of the SAT literature. From a theoretical perspective, complete algorithms can be used as tools for studying the complexities of different proof systems. From a practical point of view, these algorithms form the basis for tackling SAT problems arising from real-world applications. The practicality of modern, complete SAT solvers undoubtedly contributes to the growing interest in the class of complete SAT algorithms. We review these algorithms in this chapter, including Davis-Putnum resolution, Stalmarck’s algorithm, symbolic SAT solving, the DPLL algorithm, and modern clause-learning SAT solvers. We also discuss the issue of certifying the answers of modern complete SAT solvers.