Zchaff2004: An Efficient SAT Solver

Mahajan, Yogesh S.; Fu, Zhaohui; Malik, Sharad

doi:10.1007/11527695_27

Cited by 111 publications

(82 citation statements)

References 25 publications

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“…Runs of the d-DNNF compiler and the SAT solver were limited to 2 hours and 1.8Gb of memory. The d-DNNF compiler is Darwiche's c2d v2.18 [14], while the SAT solver is siege_v4 [15] except for very large CNFs that would not load, and where zChaff [16] was used instead. We used the same suite of problems as [8] and [11]; some of the problems being originally from [12] and [29].…”

Section: Resultsmentioning

confidence: 99%

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Mapping Conformant Planning into SAT Through Compilation and Projection

Palacios

Geffner

2006

Current Topics in Artificial Intelligence

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Abstract. Conformant planning is a variation of classical AI planning where the initial state is partially known and actions can have nondeterministic effects. While a classical plan must achieve the goal from a given initial state using deterministic actions, a conformant plan must achieve the goal in the presence of uncertainty in the initial state and action effects. Conformant planning is computationally harder than classical planning, and unlike classical planning, cannot be reduced polynomially to SAT (unless P = NP). Current SAT approaches to conformant planning, such as those considered by Giunchiglia and colleagues, thus follow a generate-and-test strategy: the models of the theory are generated one by one using a SAT solver (assuming a given planning horizon), and from each such model, a candidate conformant plan is extracted and tested for validity using another SAT call. This works well when the theory has few candidate plans and models, but otherwise is too inefficient. In this paper we propose a different use of a SAT engine for computing conformant plans where the existence of conformant plans and their extraction is carried out by means of a single SAT call over a transformed theory. This transformed theory is obtained by projecting the original theory over the action variables. This operation, while intractable, can be done efficiently provided that the original theory is compiled into DNNF (Darwiche 2000), a form akin to OBDDs (Bryant 1992). The experiments that are reported show that the resulting compile-project-sat planner is competitive with state-of-the-art optimal conformant planners and improves upon a planner recently reported at ICAPS-05. The techniques apply in direct fashion to the solution of QBFs of the form ∃x ∀y ∃z φ which are in correspondence to the problem of verifying the existence of conformant plans for a given horizon.

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Section: Resultsmentioning

confidence: 99%

“…An appealing feature of the new conformant planning scheme is that it is based on the two off-theshelf components: a d-DNNF compiler and a SAT solver. For the experiments we used the d-DNNF compiler reported in [14] and the SAT solvers reported in [15,16].…”

Section: Introductionmentioning

confidence: 99%

Mapping Conformant Planning into SAT Through Compilation and Projection

Palacios

Geffner

2006

Current Topics in Artificial Intelligence

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“…We concentrate on zchaff rand 's version of shrinking, since it was shown to be more useful in [5], and also performed better in the SAT'04 competition [6]. Suppose Chaff encounters a conflict.…”

Section: Algorithmic Details and New Heuristicsmentioning

confidence: 99%

“…Shrinking was implemented in the 2004 version of the Chaff SAT solver [5] with important modifications in each one of these components, as described below.…”

Section: Introductionmentioning

confidence: 99%

Assignment Stack Shrinking

Nadel

Ryvchin

2010

Theory and Applications of Satisfiability Testing – SAT 2010

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Abstract. Assignment stack shrinking is a technique that is intended to speed up the performance of modern complete SAT solvers. Shrinking was shown to be efficient in SAT'04 competition winners Jerusat and Chaff. However, existing studies lack the details of the shrinking algorithm. In addition, shrinking's performance was not tested in conjunction with the most modern techniques. This paper provides a detailed description of the shrinking algorithm and proposes two new heursitics for it. We show that using shrinking is critical for solving well-known industrial benchmark families with the latest versions of Minisat and Eureka.

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“…6) An ALLOY API call is made to transform the first-order relational logic signatures, facts, predicates and model completion parameters to a finite Boolean CNF. 7) We then use the ALLOY API to invoke a third-party SAT solver such as ZChaff [36] or Minisat [25] [26] to solve the Boolean CNF. 8) If a solution exists, then it is stored as an XML file.…”

Section: Model Completion Processmentioning

confidence: 99%

Towards Domain-specific Model Editors with Automatic Model Completion

2009

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Integrated development environments such as Eclipse allow users to write programs quickly by presenting a set of recommendations for code completion. Similarly, word processing tools such as Microsoft Word present corrections for grammatical errors in sentences. Both of these existing structure editors use a set of constraints expressed in the form of a natural language grammar to restrict/correct the user (syntax-directed editing) or formal grammar (language-directed editing) to aid document completion. Taking this idea further, in this paper we present an integrated software system capable of generating recommendations for model completion of partial models built in editors for domain-specific modelling languages. We present a methodology to synthesize model editors equipped with automatic completion from a modelling language's declarative specification consisting of a metamodel with a visual syntax. This meta-model directed completion feature is powered by a first-order relational logic engine implemented in ALLOY. We incorporate automatic completion in the generative tool AToM 3 . We use the Finite State Machines modelling language as a concise running example. Our approach leverages a correct by construction philosophy that renders subsequent simulation of models considerably less error-prone.

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Zchaff2004: An Efficient SAT Solver

Cited by 111 publications

References 25 publications

Mapping Conformant Planning into SAT Through Compilation and Projection

Mapping Conformant Planning into SAT Through Compilation and Projection

Assignment Stack Shrinking

Towards Domain-specific Model Editors with Automatic Model Completion

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