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In this paper we study the problem of engineering a robust solver for quantified Boolean formulas (QBFs), i.e., a tool that can efficiently solve formulas across different problem domains without the need for domain-specific tuning. The paper presents two main empirical results along this line of research. Our first result is the development of a(More)
In this paper we compare the performance of all the currently available suites to evaluate and certify QBFs. Our aim is to assess the current state of the art, and also to understand to which extent QBF encodings can be evaluated producing certificates that can be checked in a reliable and efficient way. We conclude that, while the evaluation of some QBFs(More)
We present a formal framework for (minimal) module extraction based on an abstract notion of inseparability w.r.t. a signature between ontologies. Two instances of this framework are discussed in detail for DL-Lite ontologies: concept inseparability, when ontologies imply the same complex concept inclusions over the signature, and query inseparability, when(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)