41 research outputs found
Automated Benchmarking of Incremental SAT and QBF Solvers
Incremental SAT and QBF solving potentially yields improvements when
sequences of related formulas are solved. An incremental application is usually
tailored towards some specific solver and decomposes a problem into incremental
solver calls. This hinders the independent comparison of different solvers,
particularly when the application program is not available. As a remedy, we
present an approach to automated benchmarking of incremental SAT and QBF
solvers. Given a collection of formulas in (Q)DIMACS format generated
incrementally by an application program, our approach automatically translates
the formulas into instructions to import and solve a formula by an incremental
SAT/QBF solver. The result of the translation is a program which replays the
incremental solver calls and thus allows to evaluate incremental solvers
independently from the application program. We illustrate our approach by
different hardware verification problems for SAT and QBF solvers.Comment: camera-ready version (8 pages + 2 pages appendix), to appear in the
proceedings of the 20th International Conference on Logic for Programming,
Artificial Intelligence and Reasoning (LPAR), LNCS, Springer, 201
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
Incrementally Computing Minimal Unsatisfiable Cores of QBFs via a Clause Group Solver API
We consider the incremental computation of minimal unsatisfiable cores (MUCs)
of QBFs. To this end, we equipped our incremental QBF solver DepQBF with a
novel API to allow for incremental solving based on clause groups. A clause
group is a set of clauses which is incrementally added to or removed from a
previously solved QBF. Our implementation of the novel API is related to
incremental SAT solving based on selector variables and assumptions. However,
the API entirely hides selector variables and assumptions from the user, which
facilitates the integration of DepQBF in other tools. We present implementation
details and, for the first time, report on experiments related to the
computation of MUCs of QBFs using DepQBF's novel clause group API.Comment: (fixed typo), camera-ready version, 6-page tool paper, to appear in
proceedings of SAT 2015, LNCS, Springe
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
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
SAT-Based Methods for Circuit Synthesis
Reactive synthesis supports designers by automatically constructing correct
hardware from declarative specifications. Synthesis algorithms usually compute
a strategy, and then construct a circuit that implements it. In this work, we
study SAT- and QBF-based methods for the second step, i.e., computing circuits
from strategies. This includes methods based on QBF-certification,
interpolation, and computational learning. We present optimizations, efficient
implementations, and experimental results for synthesis from safety
specifications, where we outperform BDDs both regarding execution time and
circuit size. This is an extended version of [2], with an additional appendix.Comment: Extended version of a paper at FMCAD'1
QMusExt: A Minimal (Un)satisfiable Core Extractor for Quantified Boolean Formulas
In this paper, we present QMusExt, a tool for the extraction of minimal unsatisfiable sets (MUS) from quantified Boolean formulas (QBFs) in prenex conjunctive normal form (PCNF). Our tool generalizes an efficient algorithm for MUS extraction from propositional formulas that analyses and rewrites resolution proofs generated by SAT solvers.
In addition to extracting unsatisfiable cores from false formulas in PCNF, we apply QMusExt also to obtain satisfiable cores from Q-resolution proofs of true formulas in prenex disjunctive normal form (PDNF)
SAT-Based Synthesis Methods for Safety Specs
Automatic synthesis of hardware components from declarative specifications is
an ambitious endeavor in computer aided design. Existing synthesis algorithms
are often implemented with Binary Decision Diagrams (BDDs), inheriting their
scalability limitations. Instead of BDDs, we propose several new methods to
synthesize finite-state systems from safety specifications using decision
procedures for the satisfiability of quantified and unquantified Boolean
formulas (SAT-, QBF- and EPR-solvers). The presented approaches are based on
computational learning, templates, or reduction to first-order logic. We also
present an efficient parallelization, and optimizations to utilize reachability
information and incremental solving. Finally, we compare all methods in an
extensive case study. Our new methods outperform BDDs and other existing work
on some classes of benchmarks, and our parallelization achieves a super-linear
speedup. This is an extended version of [5], featuring an additional appendix.Comment: Extended version of a paper at VMCAI'1