74 research outputs found
DepQBF 6.0: A Search-Based QBF Solver Beyond Traditional QCDCL
We present the latest major release version 6.0 of the quantified Boolean
formula (QBF) solver DepQBF, which is based on QCDCL. QCDCL is an extension of
the conflict-driven clause learning (CDCL) paradigm implemented in state of the
art propositional satisfiability (SAT) solvers. The Q-resolution calculus
(QRES) is a QBF proof system which underlies QCDCL. QCDCL solvers can produce
QRES proofs of QBFs in prenex conjunctive normal form (PCNF) as a byproduct of
the solving process. In contrast to traditional QCDCL based on QRES, DepQBF 6.0
implements a variant of QCDCL which is based on a generalization of QRES. This
generalization is due to a set of additional axioms and leaves the original
Q-resolution rules unchanged. The generalization of QRES enables QCDCL to
potentially produce exponentially shorter proofs than the traditional variant.
We present an overview of the features implemented in DepQBF and report on
experimental results which demonstrate the effectiveness of generalized QRES in
QCDCL.Comment: 12 pages + appendix; to appear in the proceedings of CADE-26, LNCS,
Springer, 201
Understanding the Relative Strength of QBF CDCL Solvers and QBF Resolution
QBF solvers implementing the QCDCL paradigm are powerful algorithms that successfully tackle many computationally complex applications. However, our theoretical understanding of the strength and limitations of these QCDCL solvers is very limited.
In this paper we suggest to formally model QCDCL solvers as proof systems. We define different policies that can be used for decision heuristics and unit propagation and give rise to a number of sound and complete QBF proof systems (and hence new QCDCL algorithms). With respect to the standard policies used in practical QCDCL solving, we show that the corresponding QCDCL proof system is incomparable (via exponential separations) to Q-resolution, the classical QBF resolution system used in the literature. This is in stark contrast to the propositional setting where CDCL and resolution are known to be p-equivalent.
This raises the question what formulas are hard for standard QCDCL, since Q-resolution lower bounds do not necessarily apply to QCDCL as we show here. In answer to this question we prove several lower bounds for QCDCL, including exponential lower bounds for a large class of random QBFs.
We also introduce a strengthening of the decision heuristic used in classical QCDCL, which does not necessarily decide variables in order of the prefix, but still allows to learn asserting clauses. We show that with this decision policy, QCDCL can be exponentially faster on some formulas.
We further exhibit a QCDCL proof system that is p-equivalent to Q-resolution. In comparison to classical QCDCL, this new QCDCL version adapts both decision and unit propagation policies
Conformant Planning as a Case Study of Incremental QBF Solving
We consider planning with uncertainty in the initial state as a case study of
incremental quantified Boolean formula (QBF) solving. We report on experiments
with a workflow to incrementally encode a planning instance into a sequence of
QBFs. To solve this sequence of incrementally constructed QBFs, we use our
general-purpose incremental QBF solver DepQBF. Since the generated QBFs have
many clauses and variables in common, our approach avoids redundancy both in
the encoding phase and in the solving phase. Experimental results show that
incremental QBF solving outperforms non-incremental QBF solving. Our results
are the first empirical study of incremental QBF solving in the context of
planning and motivate its use in other application domains.Comment: added reference to extended journal article; revision (camera-ready,
to appear in the proceedings of AISC 2014, volume 8884 of LNAI, Springer
Incremental QBF Solving
We consider the problem of incrementally solving a sequence of quantified
Boolean formulae (QBF). Incremental solving aims at using information learned
from one formula in the process of solving the next formulae in the sequence.
Based on a general overview of the problem and related challenges, we present
an approach to incremental QBF solving which is application-independent and
hence applicable to QBF encodings of arbitrary problems. We implemented this
approach in our incremental search-based QBF solver DepQBF and report on
implementation details. Experimental results illustrate the potential benefits
of incremental solving in QBF-based workflows.Comment: revision (camera-ready, to appear in the proceedings of CP 2014,
LNCS, Springer
Understanding the Relative Strength of QBF CDCL Solvers and QBF Resolution
QBF solvers implementing the QCDCL paradigm are powerful algorithms that
successfully tackle many computationally complex applications. However, our
theoretical understanding of the strength and limitations of these QCDCL
solvers is very limited.
In this paper we suggest to formally model QCDCL solvers as proof systems. We
define different policies that can be used for decision heuristics and unit
propagation and give rise to a number of sound and complete QBF proof systems
(and hence new QCDCL algorithms). With respect to the standard policies used in
practical QCDCL solving, we show that the corresponding QCDCL proof system is
incomparable (via exponential separations) to Q-resolution, the classical QBF
resolution system used in the literature. This is in stark contrast to the
propositional setting where CDCL and resolution are known to be p-equivalent.
This raises the question what formulas are hard for standard QCDCL, since
Q-resolution lower bounds do not necessarily apply to QCDCL as we show here. In
answer to this question we prove several lower bounds for QCDCL, including
exponential lower bounds for a large class of random QBFs.
We also introduce a strengthening of the decision heuristic used in classical
QCDCL, which does not necessarily decide variables in order of the prefix, but
still allows to learn asserting clauses. We show that with this decision
policy, QCDCL can be exponentially faster on some formulas.
We further exhibit a QCDCL proof system that is p-equivalent to Q-resolution.
In comparison to classical QCDCL, this new QCDCL version adapts both decision
and unit propagation policies
Efficient local search for Pseudo Boolean Optimization
Algorithms and the Foundations of Software technolog
Partial Quantifier Elimination
We consider the problem of Partial Quantifier Elimination (PQE). Given
formula exists(X)[F(X,Y) & G(X,Y)], where F, G are in conjunctive normal form,
the PQE problem is to find a formula F*(Y) such that F* & exists(X)[G] is
logically equivalent to exists(X)[F & G]. We solve the PQE problem by
generating and adding to F clauses over the free variables that make the
clauses of F with quantified variables redundant. The traditional Quantifier
Elimination problem (QE) is a special case of PQE where G is empty so all
clauses of the input formula with quantified variables need to be made
redundant. The importance of PQE is twofold. First, many problems are more
naturally formulated in terms of PQE rather than QE. Second, in many cases PQE
can be solved more efficiently than QE. We describe a PQE algorithm based on
the machinery of dependency sequents and give experimental results showing the
promise of PQE
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