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We present a novel approach to solving Quantified Boolean Formulas (QBF) that combines a search-based QBF solver with machine learning techniques. We show how classification methods can be used to predict run-times and to choose optimal heuristics both within a portfolio-based, and within a dynamic, online approach. In the dynamic method variables are set(More)
Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instance to be solved. Algorithm portfolios aim to combine the strengths of multiple algorithmic approaches by training a classifier that selects or schedules solvers dependent on the given instance. We devise a new classifier(More)
Algorithm portfolios aim to increase the robustness of our ability to solve problems efficiently. While recently proposed algorithm selection methods come ever closer to identifying the most appropriate solver given an input instance, they are bound to make wrong and, at times, costly decisions. Solver scheduling has been proposed to boost the performance(More)
The computing industry is currently facing a major architectural shift. Extra computing power is not coming anymore from higher processor frequencies , but from a growing number of computing cores and processors. For AI, and constraint solving in particular, this raises the question of how to scale current solving techniques to massively parallel(More)
Sequential algorithm portfolios for satisfiability testing (SAT), such as SATzilla and 3S, have enjoyed much success in the last decade. By leveraging the differing strengths of individual SAT solvers, portfolios employing older solvers have often fared as well or better than newly designed ones, in several categories of the annual SAT Competitions and(More)
(a) distance-based weighting (b) clustering (c) solver scheduling Challenging benchmark: a mix of 5567 application, crafted, and random instances from SAT Competitions 2002-2009; split 10-ways into 70-30 training-test datasets in a " realistic " / " mean " fashion: complete instance families missing from training! Motivation SAT community has produced(More)
QBF is the problem of deciding the satisfiability of quantified boolean formulae in which variables can be either universally or existentially quantified. QBF generalizes SAT (SAT is QBF under the restriction all variables are exis-tential) and is in practice much harder to solve than SAT. One of the sources of added complexity in QBF arises from the(More)
Binary clause reasoning has found some successful applications in SAT, and it is natural to investigate its use in various extensions of SAT. In this paper we investigate the use of binary clause reasoning in the context of solving Quantified Boolean Formulas (QBF). We develop a DPLL based QBF solver that employs extended binary clause reasoning(More)
We consider the task of assigning experts from a portfolio of specialists in order to solve a set of tasks. We apply a Bayesian model which combines collaborative filtering with a feature-based description of tasks and experts to yield a general framework for managing a portfolio of experts. The model learns an embedding of tasks and problems into a latent(More)
When tackling a computationally challenging combinatorial problem, one often observes that some solution approaches work well on some instances, while other approaches work better on other instances. This observation has given rise to the idea of building algorithm portfolios [5]. Leyton-Brown et al. [1], for instance, proposed to select one of the(More)