ABSTRACT. We show how knowledge compilation can be used as a tool for QBF. More precisely, we show that certain one can apply quantification on certain data structures used in knowledge compilation which in combination with the fact that restricted classes of CNF-formulas can be compiled into these data structures can be used to show fixed-parameter tractable results for QBF.

ABSTRACT. Quantified Boolean Formulas (QBFs) are a generalization of propositional formulae that admits succinct encodings of verification and synthesis problems. Given that modern QBF solvers are based on different architectures with complementary performance characteristics, a portfolio-based approach to QBF solving is particularly promising.

We define a natural set of features of circuit QBFs and show that they can be used to construct portfolio-based algorithm selectors of state-of-the-art circuit QBF solvers that are close to the virtual best solver. We further demonstrate that most of this performance can be achieved using surprisingly small subsets of cheaply computable and intuitive features.

ABSTRACT. Towards the Semantics of QBF Clauses

ABSTRACT. Recent solver research has developed powerful QBF solvers. Alas, we know of few tools that provide a modelling language on a higher level, translating this to QBF. This is surprising, as in the closely related field of SAT solvers, research has gone hand in hand with the develop- ment of such systems. This extended abstract on work in progress reports on a system that allows the use of second-order logic as a high-level modelling language and that grounds (translates) models written in such a language to a QBF formula. We provide an example encoding, outline the grounding process and propose further research and experiments.