21 research outputs found
QRAT+: Generalizing QRAT by a More Powerful QBF Redundancy Property
The QRAT (quantified resolution asymmetric tautology) proof system simulates
virtually all inference rules applied in state of the art quantified Boolean
formula (QBF) reasoning tools. It consists of rules to rewrite a QBF by adding
and deleting clauses and universal literals that have a certain redundancy
property. To check for this redundancy property in QRAT, propositional unit
propagation (UP) is applied to the quantifier free, i.e., propositional part of
the QBF. We generalize the redundancy property in the QRAT system by QBF
specific UP (QUP). QUP extends UP by the universal reduction operation to
eliminate universal literals from clauses. We apply QUP to an abstraction of
the QBF where certain universal quantifiers are converted into existential
ones. This way, we obtain a generalization of QRAT we call QRAT+. The
redundancy property in QRAT+ based on QUP is more powerful than the one in QRAT
based on UP. We report on proof theoretical improvements and experimental
results to illustrate the benefits of QRAT+ for QBF preprocessing.Comment: preprint of a paper to be published at IJCAR 2018, LNCS, Springer,
including appendi
Skolem Functions for Factored Formulas
Given a propositional formula F(x,y), a Skolem function for x is a function
\Psi(y), such that substituting \Psi(y) for x in F gives a formula semantically
equivalent to \exists F. Automatically generating Skolem functions is of
significant interest in several applications including certified QBF solving,
finding strategies of players in games, synthesising circuits and bit-vector
programs from specifications, disjunctive decomposition of sequential circuits
etc. In many such applications, F is given as a conjunction of factors, each of
which depends on a small subset of variables. Existing algorithms for Skolem
function generation ignore any such factored form and treat F as a monolithic
function. This presents scalability hurdles in medium to large problem
instances. In this paper, we argue that exploiting the factored form of F can
give significant performance improvements in practice when computing Skolem
functions. We present a new CEGAR style algorithm for generating Skolem
functions from factored propositional formulas. In contrast to earlier work,
our algorithm neither requires a proof of QBF satisfiability nor uses
composition of monolithic conjunctions of factors. We show experimentally that
our algorithm generates smaller Skolem functions and outperforms
state-of-the-art approaches on several large benchmarks.Comment: Full version of FMCAD 2015 conference publicatio
Proof Complexity for Quantified Boolean Formulas
Quantified Boolean formulas (QBF) extend the propositional satisfiability problem by allowing variables to be universally as well as existentially quantified. Deciding whether a QBF is true or false is PSPACE-complete and a wide range of mathematical and industrial problems can be expressed as QBFs. QBF proof complexity is the theoretical analysis of algorithmic techniques for solving QBFs.
We make a detailed comparison of the proof systems Q-Res, QU-Res, and ∀Exp + Res which extend propositional Resolution with different rules for reasoning about universally quantified variables. We give new simulation and separation results between these proof systems under two natural restrictions, when the proofs are tree-like, and when the QBFs have bounded quantifier complexity.
We consider a strong QBF proof system, QRAT, proposed as a universal proof checking format. We show that, unless P = PSPACE, QRAT does not admit strategy extraction. This is proved by constructing a family of QBFs that have short QRAT proofs but whose strategies are hard to compute in general. We also explore why strategy extraction fails for QRAT, including presenting a restricted version of QRAT which does admit strategy extraction.
We study two results from propositional proof complexity and their analogues in QBF proof complexity, showing in both cases how the additional complexity of QBF solving compared to refuting propositional formulas causes these results to fail in the QBF setting
On synthesizing Skolem functions for first order logic formulae
Skolem functions play a central role in logic, from eliminating quantifiers
in first order logic formulas to providing functional implementations of
relational specifications. While classical results in logic are only interested
in their existence, the question of how to effectively compute them is also
interesting, important and useful for several applications. In the restricted
case of Boolean propositional logic formula, this problem of synthesizing
Boolean Skolem functions has been addressed in depth, with various recent work
focussing on both theoretical and practical aspects of the problem. However,
there are few existing results for the general case, and the focus has been on
heuristical algorithms.
In this article, we undertake an investigation into the computational
hardness of the problem of synthesizing Skolem functions for first order logic
formula. We show that even under reasonable assumptions on the signature of the
formula, it is impossible to compute or synthesize Skolem functions. Then we
determine conditions on theories of first order logic which would render the
problem computable. Finally, we show that several natural theories satisfy
these conditions and hence do admit effective synthesis of Skolem functions
Efficient Certified Resolution Proof Checking
We present a novel propositional proof tracing format that eliminates complex
processing, thus enabling efficient (formal) proof checking. The benefits of
this format are demonstrated by implementing a proof checker in C, which
outperforms a state-of-the-art checker by two orders of magnitude. We then
formalize the theory underlying propositional proof checking in Coq, and
extract a correct-by-construction proof checker for our format from the
formalization. An empirical evaluation using 280 unsatisfiable instances from
the 2015 and 2016 SAT competitions shows that this certified checker usually
performs comparably to a state-of-the-art non-certified proof checker. Using
this format, we formally verify the recent 200 TB proof of the Boolean
Pythagorean Triples conjecture
Building Strategies into QBF Proofs
Strategy extraction is of great importance for quantified Boolean formulas (QBF), both in solving and proof complexity. So far in the QBF literature, strategy extraction has been algorithmically performed from proofs. Here we devise the first QBF system where (partial) strategies are built into the proof and are piecewise constructed by simple operations along with the derivation. This has several advantages: (1) lines of our calculus have a clear semantic meaning as they are accompanied by semantic objects; (2) partial strategies are represented succinctly (in contrast to some previous approaches); (3) our calculus has strategy extraction by design; and (4) the partial strategies allow new sound inference steps which are disallowed in previous central QBF calculi such as Q-Resolution and long-distance Q-Resolution. The last item (4) allows us to show an exponential separation between our new system and the previously studied reductionless long-distance resolution calculus. Our approach also naturally lifts to dependency QBFs (DQBF), where it yields the first sound and complete CDCL-style calculus for DQBF, thus opening future avenues into CDCL-based DQBF solving
Symbolic reactive synthesis
In this thesis, we develop symbolic algorithms for the synthesis of reactive systems. Synthesis, that is the task of deriving correct-by-construction implementations from formal specifications, has the potential to eliminate the need for the manual—and error-prone—programming task. The synthesis problem can be formulated as an infinite two-player game, where the system player has the objective to satisfy the specification against all possible actions of the environment player. The standard synthesis algorithms represent the underlying synthesis game explicitly and, thus, they scale poorly with respect to the size of the specification. We provide an algorithmic framework to solve the synthesis problem symbolically. In contrast to the standard approaches, we use a succinct representation of the synthesis game which leads to improved scalability in terms of the symbolically represented parameters. Our algorithm reduces the synthesis game to the satisfiability problem of quantified Boolean formulas (QBF) and dependency quantified Boolean formulas (DQBF). In the encodings, we use propositional quantification to succinctly represent different parts of the implementation, such as the state space and the transition function. We develop highly optimized satisfiability algorithms for QBF and DQBF. Based on a counterexample-guided abstraction refinement (CEGAR) loop, our algorithms avoid an exponential blow-up by using the structure of the underlying symbolic encodings. Further, we extend the solving algorithms to extract certificates in the form of Boolean functions, from which we construct implementations for the synthesis problem. Our empirical evaluation shows that our symbolic approach significantly outperforms previous explicit synthesis algorithms with respect to scalability and solution quality.In dieser Dissertation werden symbolische Algorithmen für die Synthese von reaktiven Systemen entwickelt. Synthese, d.h. die Aufgabe, aus formalen Spezifikationen korrekte Implementierungen abzuleiten, hat das Potenzial, die manuelle und fehleranfällige Programmierung überflüssig zu machen. Das Syntheseproblem kann als unendliches Zweispielerspiel verstanden werden, bei dem der Systemspieler das Ziel hat, die Spezifikation gegen alle möglichen Handlungen des Umgebungsspielers zu erfüllen. Die Standardsynthesealgorithmen stellen das zugrunde liegende Synthesespiel explizit dar und skalieren daher schlecht in Bezug auf die Größe der Spezifikation. Diese Arbeit präsentiert einen algorithmischen Ansatz, der das Syntheseproblem symbolisch löst. Im Gegensatz zu den Standardansätzen wird eine kompakte Darstellung des Synthesespiels verwendet, die zu einer verbesserten Skalierbarkeit der symbolisch dargestellten Parameter führt. Der Algorithmus reduziert das Synthesespiel auf das Erfüllbarkeitsproblem von quantifizierten booleschen Formeln (QBF) und abhängigkeitsquantifizierten booleschen Formeln (DQBF). In den Kodierungen verwenden wir propositionale Quantifizierung, um verschiedene Teile der Implementierung, wie den Zustandsraum und die Übergangsfunktion, kompakt darzustellen. Wir entwickeln hochoptimierte Erfüllbarkeitsalgorithmen für QBF und DQBF. Basierend auf einer gegenbeispielgeführten Abstraktionsverfeinerungsschleife (CEGAR) vermeiden diese Algorithmen ein exponentielles Blow-up, indem sie die Struktur der zugrunde liegenden symbolischen Kodierungen verwenden. Weiterhin werden die Lösungsalgorithmen um Zertifikate in Form von booleschen Funktionen erweitert, aus denen Implementierungen für das Syntheseproblem abgeleitet werden. Unsere empirische Auswertung zeigt, dass unser symbolischer Ansatz die bisherigen expliziten Synthesealgorithmen in Bezug auf Skalierbarkeit und Lösungsqualität deutlich übertrifft