Boolean satisfiability problem ≈ Boolean satisfiability problem
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Learning a SAT Solver from Single-Bit Supervision Open
We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solv…
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Functional Dependencies for Graphs Open
We propose a class of functional dependencies for graphs, referred to as GFDs. GFDs capture both attribute-value dependencies and topological structures of entities, and subsume conditional functional dependencies (CFDs) as a special case.…
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Accelerating the Search of Differential and Linear Characteristics with the SAT Method Open
The introduction of the automatic search boosts the cryptanalysis of symmetric-key primitives to some degree. However, the performance of the automatic search is not always satisfactory for the search of long trails or ciphers with large s…
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Strix: Explicit Reactive Synthesis Strikes Back! Open
Strix is a new tool for reactive LTL synthesis combining a direct translation of LTL formulas into deterministic parity automata (DPA) and an efficient, multi-threaded explicit state solver for parity games. In brief, Strix (1) decomposes …
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SMT Attack: Next Generation Attack on Obfuscated Circuits with Capabilities and Performance Beyond the SAT Attacks Open
In this paper, we introduce the Satisfiability Modulo Theory (SMT) attack on obfuscated circuits. The proposed attack is the superset of Satisfiability (SAT) attack, with many additional features. It uses one or more theory solvers in addi…
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BIRD: Engineering an Efficient CNF-XOR SAT Solver and Its Applications to Approximate Model Counting Open
Given a Boolean formula φ, the problem of model counting, also referred to as #SAT is to compute the number of solutions of φ. Model counting is a fundamental problem in artificial intelligence with a wide range of applications including p…
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Improved Exact Solver for the Weighted MAX-SAT Problem Open
Many exact Max-SAT solvers use a branch and bound algorithm, where the lower bound is calculated with a combination of Max-SAT resolution and detection of disjoint inconsistent subformulas. We propose a propagation algorithm which improves…
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Discrete Optimization with Decision Diagrams Open
We propose a general branch-and-bound algorithm for discrete optimization in which binary decision diagrams (BDDs) play the role of the traditional linear programming relaxation. In particular, relaxed BDD representations of the problem pr…
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Exact Synthesis of Majority-Inverter Graphs and Its Applications Open
We propose effective algorithms for exact synthesis of Boolean logic networks using satisfiability modulo theories (SMT) solvers. Since exact synthesis is a difficult problem, it can only be applied efficiently to very small functions, hav…
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SAT Competition 2016: Recent Developments Open
We give an overview of SAT Competition 2016, the 2016 edition of thefamous competition for Boolean satisfiability (SAT) solvers with over 20 years of history. A key aim is to point out ``what's hot'' in SAT competitions in 2016, i.e., new …
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SAT Competition 2020 Open
The SAT Competitions constitute a well-established series of yearly open international algorithm implementation competitions, focusing on the Boolean satisfiability (or propositional satisfiability, SAT) problem. In this article, we provid…
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Clause Elimination for SAT and QSAT Open
The famous archetypical NP-complete problem of Boolean satisfiability (SAT) and its PSPACE-complete generalization of quantified Boolean satisfiability (QSAT) have become central declarative programming paradigms through which real-world i…
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Configuration Checking with Aspiration in Local Search for SAT Open
An interesting strategy called configuration checking (CC) was recently proposed to handle the cycling problem in local search for Minimum Vertex Cover. A natural question is whether this CC strategy also works for SAT. The direct applicat…
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On The Reasons Behind Decisions Open
Recent work has shown that some common machine learning classifiers can be compiled into Boolean circuits that have the same input-output behavior. We present a theory for unveiling the reasons behind the decisions made by Boolean classifi…
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cvc4sy: Smart and Fast Term Enumeration for Syntax-Guided Synthesis Open
We present cvc4sy, a syntax-guided synthesis (SyGuS) solver based on three bounded term enumeration strategies. The first encodes term enumeration as an extension of the quantifier-free theory of algebraic datatypes. The second is based on…
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Tinted, Detached, and Lazy CNF-XOR Solving and Its Applications to Counting and Sampling Open
Given a Boolean formula, the problem of counting seeks to estimate the number of solutions of F while the problem of uniform sampling seeks to sample solutions uniformly at random. Counting and uniform sampling are fundamental problems in …
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Unifying Search-based and Compilation-based Approaches to Multi-agent Path Finding through Satisfiability Modulo Theories Open
We unify search-based and compilation-based approaches to multi-agent path finding (MAPF) through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph to given goal vertices so that they do no…
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An Effective Learnt Clause Minimization Approach for CDCL SAT Solvers Open
Learnt clauses in CDCL SAT solvers often contain redundant literals. This may have a negative impact on performance because redundant literals may deteriorate both the effectiveness of Boolean constraint propagation and the quality of subs…
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SMT-Based Nonlinear PDDL+ Planning Open
PDDL+ planning involves reasoning about mixed discrete-continuous change over time. Nearly all PDDL+ planners assume that continuous change is linear. We present a new technique that accommodates nonlinear change by encoding problems as no…
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SMC: Satisfiability Modulo Convex Programming Open
The design of cyber-physical systems (CPSs) requires methods and tools that can efficiently reason about the interaction between discrete models, e.g., representing the behaviors of 'cyber' components, and continuous models of physical pro…
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Divide and Conquer: Towards Faster Pseudo-Boolean Solving Open
The last 20 years have seen dramatic improvements in the performance of algorithms for Boolean satisfiability---so-called SAT solvers---and today conflict-driven clause learning (CDCL) solvers are routinely used in a wide range of applicat…
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MaxSAT-Based Bi-Objective Boolean Optimization Open
We explore a maximum satisfiability (MaxSAT) based approach to bi-objective optimization. Bi-objective optimization refers to the task of finding so-called Pareto-optimal solutions in terms of two objective functions. Bi-objective optimiza…
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Solving (Weighted) Partial MaxSAT by Dynamic Local Search for SAT Open
Partial MaxSAT (PMS) generalizes SAT and MaxSAT by introducing hard clauses and soft clauses. PMS and Weighted PMS (WPMS) have many important real world applications. Local search is one popular method for solving (W)PMS. Recent studies on…
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Schur Number Five Open
We present the solution of a century-old problem known as Schur Number Five: What is the largest (natural) number n such that there exists a five-coloring of the positive numbers up to n without a monochromatic solution of the equation a +…
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Discrete Hopfield Neural Network in Restricted Maximum k-Satisfiability Logic Programming Open
Maximum k-Satisfiability (MAX-kSAT) consists of the most consistent interpretation that generate the maximum number of satisfied clauses.MAX-kSAT is an important logic representation in logic programming since not all combinatorial problem…
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Verifying Properties of Binarized Deep Neural Networks Open
Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binariz…
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More Accurate Differential Properties of LED64 and Midori64 Open
In differential cryptanalysis, a differential is more valuable than the single trail belonging to it in general. The traditional way to compute the probability of the differential is to sum the probabilities of all trails within it. The au…
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Generative Adversarial Construction of Parallel Portfolios Open
Since automatic algorithm configuration methods have been very effective, recently there is increasing research interest in utilizing them for automatic solver construction, resulting in several notable approaches. For these approaches, a …
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A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality Open
We developed a formal framework for conflict-driven clause learning (CDCL) using the Isabelle/HOL proof assistant. Through a chain of refinements, an abstract CDCL calculus is connected first to a more concrete calculus, then to a SAT solv…
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SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver Open
Integrating logical reasoning within deep learning architectures has been a major goal of modern AI systems. In this paper, we propose a new direction toward this goal by introducing a differentiable (smoothed) maximum satisfiability (MAXS…