119 research outputs found
09461 Abstracts Collection -- Algorithms and Applications for Next Generation SAT Solvers
From 8th to 13th November 2009, the Dagstuhl Seminar 09461 "Algorithms and Applications for Next Generation SAT Solvers" was held in Schloss Dagstuhl--Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts, slides or full papers are provided, if available
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
Planning as Quantified Boolean Formulae
This work explores the idea of classical Planning as Quantified Boolean Formulae. Planning as Satisfiability (SAT) is a popular approach to Planning and has been explored in detail producing many compact and efficient encodings, Planning-specific solver implementations and innovative new constraints. However, Planning as Quantified Boolean Formulae (QBF) has been relegated to conformant Planning approaches, with the exception of one encoding that has not yet been investigated in detail. QBF is a promising setting for Planning given that the problems have the same complexity. This work introduces two approaches for translating bounded propositional reachability problems into QBF. Both exploit the expressivity of the binarytree structure of the QBF problem to produce encodings that are as small as logarithmic in the size of the instance and thus exponentially smaller than the corresponding SAT encoding with the same bound. The first approach builds on the iterative squaring formulation of Rintanen; the intuition behind the idea is to recursively fold the plan around the midpoint, reducing the number of time-steps that need to be described from n to logâ‚‚n. The second approach exploits domain-level lifting to achieve significant improvements in efficiency. Experimentation was performed to compare our formulation of the first approach with the previous formulation, and to compare both approaches with comparative and state-of-the-art SAT approaches. Results presented in this work show that our formulation of the first approach is an improvement over the previous, and that both approaches produce encodings that are indeed much smaller than corresponding SAT encodings, in both terms of encoding size and memory used during solving. Evidence is also provided to show that the first approach is feasible, if not yet competitive with the state-of-the-art, and that the second approach produces superior encodings to the SAT encodings when the domain is suited to domain-level lifting.This work explores the idea of classical Planning as Quantified Boolean Formulae. Planning as Satisfiability (SAT) is a popular approach to Planning and has been explored in detail producing many compact and efficient encodings, Planning-specific solver implementations and innovative new constraints. However, Planning as Quantified Boolean Formulae (QBF) has been relegated to conformant Planning approaches, with the exception of one encoding that has not yet been investigated in detail. QBF is a promising setting for Planning given that the problems have the same complexity. This work introduces two approaches for translating bounded propositional reachability problems into QBF. Both exploit the expressivity of the binarytree structure of the QBF problem to produce encodings that are as small as logarithmic in the size of the instance and thus exponentially smaller than the corresponding SAT encoding with the same bound. The first approach builds on the iterative squaring formulation of Rintanen; the intuition behind the idea is to recursively fold the plan around the midpoint, reducing the number of time-steps that need to be described from n to logâ‚‚n. The second approach exploits domain-level lifting to achieve significant improvements in efficiency. Experimentation was performed to compare our formulation of the first approach with the previous formulation, and to compare both approaches with comparative and state-of-the-art SAT approaches. Results presented in this work show that our formulation of the first approach is an improvement over the previous, and that both approaches produce encodings that are indeed much smaller than corresponding SAT encodings, in both terms of encoding size and memory used during solving. Evidence is also provided to show that the first approach is feasible, if not yet competitive with the state-of-the-art, and that the second approach produces superior encodings to the SAT encodings when the domain is suited to domain-level lifting
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Proof complexity is a multi-disciplinary intellectual endeavor that addresses questions of the general form “how difficult is it to prove certain mathematical facts?” The current workshop focused on recent advances in our understanding of logic-based proof systems and on connections to algorithms, geometry and combinatorics research, such as the analysis of approximation algorithms, or the size of linear or semidefinite programming formulations of combinatorial optimization problems, to name just two important examples
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