40 research outputs found
Clause/Term Resolution and Learning in the Evaluation of Quantified Boolean Formulas
Resolution is the rule of inference at the basis of most procedures for
automated reasoning. In these procedures, the input formula is first translated
into an equisatisfiable formula in conjunctive normal form (CNF) and then
represented as a set of clauses. Deduction starts by inferring new clauses by
resolution, and goes on until the empty clause is generated or satisfiability
of the set of clauses is proven, e.g., because no new clauses can be generated.
In this paper, we restrict our attention to the problem of evaluating
Quantified Boolean Formulas (QBFs). In this setting, the above outlined
deduction process is known to be sound and complete if given a formula in CNF
and if a form of resolution, called Q-resolution, is used. We introduce
Q-resolution on terms, to be used for formulas in disjunctive normal form. We
show that the computation performed by most of the available procedures for
QBFs --based on the Davis-Logemann-Loveland procedure (DLL) for propositional
satisfiability-- corresponds to a tree in which Q-resolution on terms and
clauses alternate. This poses the theoretical bases for the introduction of
learning, corresponding to recording Q-resolution formulas associated with the
nodes of the tree. We discuss the problems related to the introduction of
learning in DLL based procedures, and present solutions extending
state-of-the-art proposals coming from the literature on propositional
satisfiability. Finally, we show that our DLL based solver extended with
learning, performs significantly better on benchmarks used in the 2003 QBF
solvers comparative evaluation
Property specification patterns at work: verification and inconsistency explanation
Property specification patterns (PSPs) have been proposed to ease the formalization of requirements, yet enable automated verification thereof. In particular, the internal consistency of specifications written with PSPs can be checked automatically with the use of, for example, linear temporal logic (LTL) satisfiability solvers. However, for most practical applications, the expressiveness of PSPs is too restricted to enable writing useful requirement specifications, and proving that a set of requirements is inconsistent can be worthless unless a minimal set of conflicting requirements is extracted to help designers to correct a wrong specification. In this paper, we extend PSPs by considering Boolean as well as atomic numerical assertions, we contribute an encoding from extended PSPs to LTL formulas, and we present an algorithm computing inconsistency explanations, i.e., irreducible inconsistent subsets of the original set of requirements. Our extension enables us to reason about the internal consistency of functional requirements which would not be captured by basic PSPs. Experimental results demonstrate that our approach can check and explain (in)consistencies in specifications with nearly two thousand requirements generated using a probabilistic model, and that it enables effective handling of real-world case studies
Quantifier Structure in search based procedures for QBFs
none3The best currently available solvers for quantified Boolean
formulas (QBFs) process their input in prenex form, i.e., all the quan-
tifiers have to appear in the prefix of the formula separated from the
purely propositional part representing the matrix. However, in many QBFs
derived from applications, the propositional part is intertwined with the
quantifier structure. To tackle this problem, the standard approach is to
convert such QBFs in prenex form, thereby losing structural information
about the prefix. In the case of search-based solvers, the prenex-form
conversion introduces additional constraints on the branching heuristic
and reduces the benefits of the learning mechanisms. In this paper, we
show that conversion to prenex form is not necessary: current search-based
solvers can be naturally extended in order to handle nonprenex QBFs
and to exploit the original quantifier structure. We highlight the two
mentioned drawbacks of the conversion in prenex form with a simple
example, and we show that our ideas can also be useful for solving QBFs
in prenex form. To validate our claims, we implemented our ideas in the
state-of-the-art search-based solver QUBE and conducted an extensive
experimental analysis. The results show that very substantial speedups can
be obtained.The IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) is a monthly publication. Manuscripts considered for publication should focus on algorithms, methods, techniques, and tools for the automated design of integrated circuits and systems, and on related areas. Submitted papers may be of a tutorial or research nature. Research papers must present original contributions and must show significant new material over descriptions or derivations available elsewhere. Tutorial papers should review the state-of-the-art in specific areas of CAD and, at the same time, present readers with key research perspectives and future challenges.GIUNCHIGLIA E.; NARIZZANO M.; TACCHELLA A.Giunchiglia, Enrico; Narizzano, Massimo; Tacchella, Armand
Backjumping for Quantified Boolean Logic satisfiability
The implementation of effective reasoning tools for deciding the satisfiability of Quantified
Boolean Formulas (QBFs) is an important research issue in Artificial Intelligence. Many decision
procedures have been proposed in the last few years, most of them based on the Davis, Logemann,
Loveland procedure (DLL) for propositional satisfiability (SAT). In this paper we show how it is
possible to extend the conflict-directed backjumping schema for SAT to the satisfiability of QBFs:
When applicable, conflict-directed backjumping allows search to skip over existentially quantified
literals while backtracking. We introduce solution-directed backjumping, which allows the same
behavior for universally quantified literals. We show how it is possible to incorporate both conflict-
directed and solution-directed backjumping in a DLL-based decision procedure for satisfiability of
QBFs. We also implement and test the procedure: The experimental analysis shows that, because of
backjumping, significant speed-ups can be obtained.
Summing up: We present the first algorithm that applies conflict and solution directed backjumping
to QBF, and demonstrate the performance of this algorithm via an empirical study
Quantifier Structure in search based procedures for QBFs
The best currently available solvers for quantified Boolean
formulas (QBFs) process their input in prenex form, i.e., all the quan-
tifiers have to appear in the prefix of the formula separated from the
purely propositional part representing the matrix. However, in many QBFs
derived from applications, the propositional part is intertwined with the
quantifier structure. To tackle this problem, the standard approach is to
convert such QBFs in prenex form, thereby losing structural information
about the prefix. In the case of search-based solvers, the prenex-form
conversion introduces additional constraints on the branching heuristic
and reduces the benefits of the learning mechanisms. In this paper, we
show that conversion to prenex form is not necessary: current search-based
solvers can be naturally extended in order to handle nonprenex QBFs
and to exploit the original quantifier structure. We highlight the two
mentioned drawbacks of the conversion in prenex form with a simple
example, and we show that our ideas can also be useful for solving QBFs
in prenex form. To validate our claims, we implemented our ideas in the
state-of-the-art search-based solver QUBE and conducted an extensive
experimental analysis. The results show that very substantial speedups can
be obtained
Quantifier Structure in search based procedures for QBFs
The best currently available solvers for quantified Boolean
formulas (QBFs) process their input in prenex form, i.e., all the quan-
tifiers have to appear in the prefix of the formula separated from the
purely propositional part representing the matrix. However, in many QBFs
derived from applications, the propositional part is intertwined with the
quantifier structure. To tackle this problem, the standard approach is to
convert such QBFs in prenex form, thereby losing structural information
about the prefix. In the case of search-based solvers, the prenex-form
conversion introduces additional constraints on the branching heuristic
and reduces the benefits of the learning mechanisms. In this paper, we
show that conversion to prenex form is not necessary: current search-based
solvers can be naturally extended in order to handle nonprenex QBFs
and to exploit the original quantifier structure. We highlight the two
mentioned drawbacks of the conversion in prenex form with a simple
example, and we show that our ideas can also be useful for solving QBFs
in prenex form. To validate our claims, we implemented our ideas in the
state-of-the-art search-based solver QUBE and conducted an extensive
experimental analysis. The results show that very substantial speedups can
be obtained