Formulas In Conjunctive Normal Form Research Articles

Extracting a DPLL Algorithm

We formalize a completeness proof for the DPLL proof system and extract a DPLL SAT solver from it. When applied to a propositional formula in conjunctive normal form the program produces either a satisfying assignment or a DPLL derivation which shows that it is unsatisfiable. We use non-computational quantifiers to remove redundant computational content from the extracted program and improve its performance. The formalization is carried out in the Minlog system.

A finite state intersection approach to propositional satisfiability

We use a finite state (FSA) construction approach to address the problem of propositional satisfiability (SAT). We present a very simple translation from formulas in conjunctive normal form (CNF) to regular expressions and use regular expressions to construct an FSA. As a consequence of the FSA construction, we obtain an ALL-SAT solver and model counter. This automata construction can be considered essentially a finite state intersection grammar (FSIG). We also show how an FSIG approach can be encoded. Several variable ordering (state ordering) heuristics are compared in terms of the running time of the FSA and FSIG construction. We also present a strategy for clause ordering (automata composition). Running times of state-of-the-art model counters and BDD based SAT solvers are compared and we show that both the FSA and FSIG approaches obtain an state-of-the-art performance on some hard unsatisfiable benchmarks. It is also shown that clause learning techniques can help improve performance. This work brings up many questions on the possible use of automata and grammar models to address SAT.

Disproof of the neighborhood conjecture with implications to SAT

We study a special class of binary trees. Our results have implications on Maker/Breaker games and SAT: We disprove a conjecture of Beck on positional games and construct an unsatisfiable k-CNF formula with few occurrences per variable, thereby improving a previous result by Hoory and Szeider and showing that the bound obtained from the Lovasz Local Lemma is tight up to a constant factor. A (k, s)-CNF formula is a boolean formula in conjunctive normal form where every clause contains exactly k distinct literals and every variable occurs in at most s clauses. The (k, s)-SAT problem is the satisfiability problem restricted to (k, s)-CNF formulas. Kratochvil, Savický and Tuza showed that for every k?3 there is an integer f(k) such that every (k, f(k))-CNF formula is satisfiable, but (k, f(k) + 1)-SAT is already NP-complete (it is not known whether f(k) is computable). Kratochvil, Savický and Tuza also gave the best known lower bound $$f(k) = \Omega \left( {\tfrac{{2^k }} {k}} \right)$$ , which is a consequence of the Lovasz Local Lemma. We prove that, in fact, $$f(k) = \Theta \left( {\tfrac{{2^k }} {k}} \right)$$ , improving upon the best known upper bound $$O\left( {(\log k) \cdot \tfrac{{2^k }} {k}} \right)$$ by Hoory and Szeider. Finally we establish a connection between the class of trees we consider and a certain family of positional games. The Maker/Breaker game we study is as follows. Maker and Breaker take turns in choosing vertices from a given n-uniform hypergraph $$\mathcal{F}$$ , with Maker going first. Maker's goal is to completely occupy a hyperedge and Breaker tries to prevent this. The maximum neighborhood size of a hypergraph $$\mathcal{F}$$ is the maximal s such that some hyperedge of $$\mathcal{F}$$ intersects exactly s other hyperedges. Beck conjectures that if the maximum neighborhood size of $$\mathcal{F}$$ is smaller than 2 n?1 ? 1 then Breaker has a winning strategy. We disprove this conjecture by establishing, for every n?3, the existence of an n-uniform hypergraph with maximum neighborhood size 3·2 n?3 where Maker has a winning strategy. Moreover, we show how to construct, for every n, an n-uniform hypergraph with maximum degree at most $$\frac{{2^{n + 2} }} {n}$$ where Maker has a winning strategy. In addition we show that each n-uniform hypergraph with maximum degree at most $$\frac{{2^{n - 2} }} {{en}}$$ has a proper halving 2-coloring, which solves another open problem posed by Beck related to the Neighborhood Conjecture.

Towards efficient MUS extraction

Minimally Unsatisfiable Subformulas (MUS) find a wide range of practical applications, including product configuration, knowledge-based validation, and hardware and software design and verification. MUSes also find application in recent Maximum Satisfiability algorithms and in CNF formula redundancy removal. Besides direct applications in Propositional Logic, algorithms for MUS extraction have been applied to more expressive logics. This paper proposes two algorithms for computation of MUSes of propositional formulas in Conjunctive Normal Form (CNF). The first algorithm is optimal in its class, meaning that it requires the smallest number of calls to a SAT solver. The second algorithm extends earlier work, but implements a number of new techniques. Among these, this paper analyzes in detail the technique of recursive model rotation, which provides significant performance gains in practice. Experimental results, obtained on representative practical benchmarks, indicate that the new algorithms achieve significant performance gains with respect to state of the art MUS extraction algorithms.

The Complexity of Rooted Phylogeny Problems

Several computational problems in phylogenetic reconstruction can be formulated as restrictions of the following general problem: given a formula in conjunctive normal form where the literals are rooted triples, is there a rooted binary tree that satisfies the formula? If the formulas do not contain disjunctions, the problem becomes the famous rooted triple consistency problem, which can be solved in polynomial time by an algorithm of Aho, Sagiv, Szymanski, and Ullman. If the clauses in the formulas are restricted to disjunctions of negated triples, Ng, Steel, and Wormald showed that the problem remains NP-complete. We systematically study the computational complexity of the problem for all such restrictions of the clauses in the input formula. For certain restricted disjunctions of triples we present an algorithm that has sub-quadratic running time and is asymptotically as fast as the fastest known algorithm for the rooted triple consistency problem. We also show that any restriction of the general rooted phylogeny problem that does not fall into our tractable class is NP-complete, using known results about the complexity of Boolean constraint satisfaction problems. Finally, we present a pebble game argument that shows that the rooted triple consistency problem (and also all generalizations studied in this paper) cannot be solved by Datalog.

An analytic criterion for CSAT

We give a criterion for satisfiability of a boolean formula in conjunctive normal form by means of a multiple integral.

Simulating Circuit-Level Simplifications on CNF

Boolean satisfiability (SAT) and its extensions have become a core technology in many application domains, such as planning and formal verification, and continue finding various new application domains today. The SAT-based approach divides into three steps: encoding, preprocessing, and search. It is often argued that by encoding arbitrary Boolean formulas in conjunctive normal form (CNF), structural properties of the original problem are not reflected in the CNF. This should result in the fact that CNF-level preprocessing and SAT solver techniques have an inherent disadvantage compared to related techniques applicable on the level of more structural SAT instance representations such as Boolean circuits. Motivated by this, various simplification techniques and intricate CNF encodings for circuit-level SAT instance representations have been proposed. On the other hand, based on the highly efficient CNF-level clause learning SAT solvers, there is also strong support for the claim that CNF is sufficient as an input format for SAT solvers. In this work we study the effect of CNF-level simplification techniques, focusing on SatElite-style variable elimination (VE) and what we call blocked clause elimination (BCE). We show that BCE is surprisingly effective both in theory and in practice on CNF formulas resulting from a standard CNF encoding for circuits: without explicit knowledge of the underlying circuit structure, it achieves the same level of simplification as a combination of circuit-level simplifications and previously suggested polarity-based CNF encodings. We also show that VE can achieve many of the same effects as BCE, but not all. On the other hand, it turns out that VE and BCE are indeed partially orthogonal techniques. We also study the practical effects of combining BCE and VE for reducing the size of formulas and on the running times of state-of-the-art SAT solvers. Furthermore, we address the problem of how to construct original witnesses to satisfiable CNF formulas when applying a combination of BCE and VE.

Learning a subclass of k-quasi-Horn formulas with membership queries

Boolean formulas have been widely studied in the field of learning theory. We focus on the model of learning with queries, and study a restriction of the class of k- quasi-Horn formulas, that is, conjunctive normal form formulas where the number of unnegated literals per clause is at most k. This class is known to be as hard to learn as the general class CNF of conjunctive normal form formulas. By imposing some constraints, we define a fragment of this logic that can be learned using only membership queries. Also we prove that none of these constraints makes by itself the class learnable.

Constraint Satisfaction Problems in Clausal Form I: Autarkies and Deficiency

We consider the problem of generalising boolean formulas in conjunctive normal form by allowing non-boolean variables, with the goal of maintaining combinatorial properties. Requiring that a literal involves only a single variable, the most general form of literals are the wellknown “signed literals”, corresponding to unary constraints in CSP. However we argue that only the restricted form of “negative monosigned literals” and the resulting generalised clause-sets, corresponding to “sets of no-goods” in the AI literature, maintain the essential properties of boolean conjunctive normal forms. In this first part of a mini-series of two articles, we build up a solid foundation for (generalised) clause-sets, including the notion of autarky systems, the interplay between autarkies and resolution, and basic notions of (DP-)reductions. As a basic combinatorial parameter of generalised clause-sets we introduce the (generalised) notion of deficiency, which in the boolean case is the difference between the number of clauses and the number of variables. Autarky theory plays a fundamental role here, and we concentrate especially on matching autarkies (based on matching theory). A natural task is to determine the structure of (matching) lean clause-sets, which do not admit non-trivial (matching) autarkies. A central result is the computation of the lean kernel (the largest lean subset) of a (generalised) clause-set in polynomial time for bounded maximal deficiency.

On the expressive power of CNF formulas of bounded tree- and clique-width

We study representations of polynomials over a field K from the point of view of their expressive power. Three important examples for the paper are polynomials arising as permanents of bounded tree-width matrices, polynomials given via arithmetic formulas, and families of so called CNF polynomials. The latter arise in a canonical way from families of Boolean formulas in conjunctive normal form. To each such CNF formula there is a canonically attached incidence graph. Of particular interest to us are CNF polynomials arising from formulas with an incidence graph of bounded tree- or clique-width. We show that the class of polynomials arising from families of polynomial size CNF formulas of bounded tree-width is the same as those represented by polynomial size arithmetic formulas, or permanents of bounded tree-width matrices of polynomial size. Then, applying arguments from communication complexity we show that general permanent polynomials cannot be expressed by CNF polynomials of bounded tree-width. We give a similar result in the case where the clique-width of the incidence graph is bounded, but for this we need to rely on the widely believed complexity theoretic assumption # P ⊈ F P / p o l y .

Optimality of size-degree tradeoffs for polynomial calculus

There are methods to turn short refutations in polynomial calculus (Pc) and polynomial calculus with resolution (Pcr) into refutations of low degree. Bonet and Galesi [1999, 2003] asked if such size-degree tradeoffs for Pc [Clegg et al. 1996; Impagliazzo et al. 1999] and Pcr [Alekhnovich et al. 2004] are optimal. We answer this question by showing a polynomial encoding of the graph ordering principle on m variables which requires Pc and Pcr refutations of degree Ω(√ m ). Tradeoff optimality follows from our result and from the short refutations of the graph ordering principle in Bonet and Galesi [1999, 2001]. We then introduce the algebraic proof system Pcr k which combines together polynomial calculus and k-DNF resolution (Res k ). We show a size hierarchy theorem for Pcr k : Pcr k is exponentially separated from Pcr k+1 . This follows from the previous degree lower bound and from techniques developed for Res k . Finally we show that random formulas in conjunctive normal form (3-CNF) are hard to refute in Pcr k .

Making Deduction More Effective in SAT Solvers

Satisfiability (SAT) solvers often benefit from transformations of the formula to be decided that allow them to do more through deduction and decrease their reliance on enumeration. For formulae in conjunctive normal form, subsumed clauses may be removed or partial resolution may be applied. The objectives of simplifying the formula and speeding up the solver are sometimes competing. We characterize existing transformations in terms of their impact on the deductive power of the formula and their effects on the sizes of the implication graphs. For example, we show that variable elimination works by improving implication graphs. We also present two new techniques that try to increase deductive power. The first is a check performed during the computation of resolvents. The second is a new preprocessing algorithm based on distillation that combines simplification and increase of deductive power. Most current SAT solvers apply resolution at various stages to derive new clauses or simplify existing ones. The former happens during conflict analysis, while the latter is usually done during preprocessing. We show how subsumption of the operands by the resolvent can be inexpensively detected during resolution; we then discuss how this detection is used to improve three stages of the SAT solver: variable elimination, clause distillation, and conflict analysis. The “on-the-fly” subsumption check is easily integrated in a SAT solver. In particular, it is compatible with strong conflict analysis and the generation of unsatisfiability proofs. Experiments show the effectiveness of the new techniques.

A Randomized Algorithm for 3-SAT

In this work we propose and analyze a simple randomized algorithm to find a satisfiable assignment for a Boolean formula in conjunctive normal form (CNF) having at most 3 literals in every clause. Given a k-CNF formula phi on n variables, and alpha in{0,1}^n that satisfies phi, a clause of phi is critical if exactly one literal of that clause is satisfied under assignment alpha. Paturi et. al. (Chicago Journal of Theoretical Computer Science 1999) proposed a simple randomized algorithm (PPZ) for k-SAT for which success probability increases with the number of critical clauses (with respect to a fixed satisfiable solution of the input formula). Here, we first describe another simple randomized algorithm DEL which performs better if the number of critical clauses are less (with respect to a fixed satisfiable solution of the input formula). Subsequently, we combine these two simple algorithms such that the success probability of the combined algorithm is maximum of the success probabilities of PPZ and DEL on every input instance. We show that when the average number of clauses per variable that appear as unique true literal in one or more critical clauses in phi is between 1 and 1.9317, combined algorithm performs better than the PPZ algorithm.

Special Issue on Automated Deduction

A key problem in artificial intelligence is the question how to perform mathematical proofs automatically. This is not just a theoretical question, but it has numerous highly important practical applications. In particular, tools that perform automated deduction are used to verify software and hardware in safety-critical applications. For this reason, automated deduction has always been an important area within artificial intelligence. Articles on different aspects of automated deduction can be found in many issues of the KI Journal, but there have also been two special issues (in 1992 and 1998) devoted to the relationship of AI and logic and to automated deduction and its applications, respectively. These two issues also marked the beginning and the end of the DFG priority program “Deduction” which had a tremendous impact on the research in the field in Germany. The current special issue focuses on the research in automated deduction after 1998. This research can roughly be classified by the different logics under consideration. For propositional logic, the article by U. Egly and L. Haller describes SAT solving for non-clausal formulas and compares it with the more conventional SAT solving for formulas in conjunctive normal form. To apply automated deduction for knowledge representation, instead of propositional logic one usually regards specialized logics like description logics. The article of F. Baader, C. Lutz, and A.-Y. Turhan discusses the trend to new less expressive description logics

Logical and algorithmic properties of stable conditional independence

The logical and algorithmic properties of stable conditional independence (CI) as an alternative structural representation of conditional independence information are investigated. We utilize recent results concerning a complete axiomatization of stable conditional independence relative to discrete probability measures to derive perfect model properties of stable conditional independence structures. We show that stable CI can be interpreted as a generalization of Markov networks and establish a connection between sets of stable CI statements and propositional formulas in conjunctive normal form. Consequently, we derive that the implication problem for stable CI is coNP-complete. Finally, we show that Boolean satisfiability (SAT) solvers can be employed to efficiently decide the implication problem and to compute concise, non-redundant representations of stable CI, even for instances involving hundreds of random variables.

FROM XSAT TO SAT BY EXHIBITING BOOLEAN FUNCTIONS

Given a Boolean formula in conjunctive normal form (CNF), the Exact Satisfiability problem (XSAT), a variant of the Satisfiability problem (SAT), consists in finding an assignment to the variables such that each clause contains exactly one satisfied literal. Best algorithms to solve this problem run in [Formula: see text] ([Formula: see text] for X3SAT). Another possibility is to transform each clause in a set of equivalent clauses for the Satisfiability problem and to use modern and powerful solvers (zChaff, Berkmin, MiniSat, RSat etc.) to find such truth assignment. In this paper we introduce three new encodings from XSAT instances to SAT instances that lead to a lot of structural information (equivalency gates and and gates) which is naturally hidden in the pairwise transformation. Some solvers (lsat,march_dl,eqsatz) can take into account this kinds of structural information to make simplifications as pretreatment and speed-up the resolution. Then we show the interest of dealing with the XSAT formalism by introducing an encoding of binary CSP and graph coloring problem into XSAT instances. Preliminary results on real-world binary CSP and graph coloring problem show the importance of exhibiting equivalencies for the XSAT problem.

The Complexity of Problems for Quantified Constraints

In this paper we are interested in quantified propositional formulas in conjunctive normal form with “clauses” of arbitrary shapes. i.e., consisting of applying arbitrary relations to variables. We study the complexity of the evaluation problem, the model checking problem, the equivalence problem, and the counting problem for such formulas, both with and without a bound on the number of quantifier alternations. For each of these computational goals we get full complexity classifications: We determine the complexity of each of these problems depending on the set of relations allowed in the input formulas. Thus, on the one hand we exhibit syntactic restrictions of the original problems that are still computationally hard, and on the other hand we identify non-trivial subcases that admit efficient algorithms.

SymChaff: exploiting symmetry in a structure-aware satisfiability solver

This article presents a new low-overhead framework for representing and utilizing problem symmetry in propositional satisfiability algorithms. While many previous approaches have focused on symmetry extraction as a key component, the novelty in the proposed strategy lies in using high level problem description to pass on symmetry information to the SAT solver in a simple and concise form, in addition to the usual conjunctive normal form formula. This information, comprising of the so-called symmetry sets and variable classes, captures variable semantics relevant to “complete multi-class symmetry” and is utilized dynamically to prune the search space. This allows us to address many limitations of alternative approaches like symmetry breaking predicates, implicit pseudo-Boolean representations, general group-theoretic methods, and zero-suppressed binary decision diagrams. We demonstrate the efficacy of our technique through a solver called SymChaff, which achieves exponential speedup over one of the best systematic SAT solvers on problems from both theory and practice, often by simply using natural tags or annotation in the problem specification.

DNA computing approach for automated test pattern generation for digital circuits

Testing of digital circuits is a compute intensive problem. This article deals with the problem of automated test pattern generation for large digital circuits. A new evolutionary approach based on DNA computing is presented, which exploits the computational power of DNA molecules to solve the problem. A Boolean formula in conjunctive normal form is extracted from the circuit under test and then the proposed algorithm based on DNA computing is used to find the solution satisfying that formula. Exploiting the massive parallelism and recombination properties of DNA molecules, a test vector is found in polynomial time i.e., O (nk). Its effectiveness in terms of Fault coverage, CPU time and Test vector generated is compared with some existing classical approaches like simulated annealing and genetic algorithms.

SAT graph-based representation: A new perspective

In this paper a new graph based representation of Boolean formulas in conjunctive normal form (CNF) is proposed. It extends the well-known graph representation of binary CNF formulas (2-SAT) to the general case. Every clause is represented as a set of (conditional) implications and encoded with different edges labeled with a set of literals, called context. This representation admits many interesting features. For example, a path from the node labeled with a literal ¬ x to the node labeled with a literal x gives us an original way to compute the condition under which the literal x is implied. Using this representation, we show that classical resolution can be reformulated as a transitive closure on the generated graph. Interestingly enough, using the SAT graph-based representation three original applications are then derived. The first one deals with the 2-SAT strong backdoor set computation problem, whereas in the second one the underlying representation is used to derive hard SAT instances with respect to the state-of-the-art satisfiability solvers. Finally, a new preprocessing technique of CNF formulas which extends the well-known hyper-resolution rule is proposed. Experimental results show interesting improvements on many classes of SAT instances taken from the last SAT competitions.