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

A game characterisation of tree-like Q-Resolution size

We provide a characterisation for the size of proofs in tree-like Q-Resolution and tree-like QU-Resolution by a Prover–Delayer game, which is inspired by a similar characterisation for the proof size in classical tree-like Resolution. This gives one of the first successful transfers of one of the lower bound techniques for classical proof systems to QBF proof systems. We apply our technique to show the hardness of three classes of formulas for tree-like Q-Resolution. In particular, we give a proof of the hardness of the parity formulas from Beyersdorff et al. (2015) for tree-like Q-Resolution and of the formulas of Kleine Büning et al. (1995) for tree-like QU-Resolution

Exploiting Problem Structure in QBF Solving

Deciding the truth of a Quantified Boolean Formula (QBF) is a canonical PSPACE-complete problem. It provides a powerful framework for encoding problems that lie in PSPACE. These include many problems in automatic verification, and problems with discrete uncertainty or non-determinism. Two person adversarial games are another type of problem that are naturally encoded in QBF. It is standard practice to use Conjunctive Normal Form (CNF) when representing QBFs. Any propositional formula can be efficiently translated to CNF via the addition of new variables, and solvers can be implemented more efficiently due to the structural simplicity of CNF. However, the translation to CNF involves a loss of some structural information. This thesis shows that this structural information is important for efficient QBF solving, and shows how this structural information can be utilized to improve state-of-the-art QBF solving. First, a non-CNF circuit-based solver is presented. It makes use of information not present in CNF to improve its performance. We present techniques that allow it to exploit the duality between solutions and conflicts that is lost when working with CNF. This duality can also be utilized in the production of certificates, allowing both true and false formulas to have easy-to-verify certificates of the same form. Then, it is shown that most modern CNF-based solvers can benefit from the additional information derived from duality using only minor modifications. Furthermore, even partial duality information can be helpful. We show that for standard methods for conversion to CNF, some of the required information can be reconstructed from the CNF and greatly benefit the solver.Ph

Incremental plan recognition in an agent programming framework

In this paper, we propose a formal model of plan recognition for inclusion in a cognitive agent programming framework. The model is based on the Situation Calculus and the Con-Golog agent programming language. This provides a very rich plan specification language. Our account also supports incremental recognition, where the set of matching plans is progressively filtered as more actions are observed. This is specified using a transition system account. The model also supports hierarchically structured plans and recognizes subplan relationships

Exploiting QBF Duality on a Circuit Representation

Search based solvers for Quantified Boolean Formulas (QBF) have adapted the SAT solver techniques of unit propagation and clause learning to prune falsifying assignments. The technique of cube learning has been developed to help them prune satisfying assignments. Cubes, however, have not been able to achieve the same degree of effectiveness as clauses. In this paper we demonstrate how a circuit representation for QBF can support the propagation of dual truth values. The dual values support the identical techniques of unit propagation and clause learning, except now it is satisfying assignments rather than falsifying assignments that are pruned. Dual value propagation thus exploits the circuit representation and the duality of QBF formulas so that the same effective SAT techniques can now be used to prune both falsifying and satisfyingly assignments. We show empirically that dual propagation yields significantperformance improvements and advances the state of the art in QBF solving

Incremental Plan Recognition in an Agent programming Framework

Dans cet article, nous proposons un modèle formel de la reconnaissance de plans en vue de l'inclure dans un formalisme de programmation d'agent. Le modèle est basé sur le calcul des situations et le langage de programmation d'agent ConGolog. Ceci fournit un langage très riche pour la spéci- fication des plans à reconnaitre. Notre modèle supporte aussi la reconnaissance incrémentale, où l'ensemble des hypothèses de plans exécutés est filtré à mesure que les actions sont observées. Le modèle est spécifié en termes d'un système de transitions pour le langage de plans. Le modèle supporte aussi les plans structurés hiérarchiquement et reconnait les relations entre un plan et les sous-plan qu'il contient. In this paper, we propose a formal model of plan recognition for inclusion in a cognitive agent programming framework. The model is based on the Situation Calculus and the ConGolog agent programming language. This provides a very rich plan specification language. Our account also supports incremental recognition, where the set of matching plans is progressively filtered as more actions are observed. This is specified using a transition system account. The model also supports hierarchically structured plans and recognizes subplan relationships