QBF with Soft Variables

QBF formulae are usually considered in prenex form, i.e. the quantifierblock is completely separated from the propositional part of the QBF.Among others, the semantics of the QBF is defined by the sequence ofthe variables within the prefix, where existentially quantifiedvariables depend on all universally quantified variables stated to theleft.In this paper we extend that classical definition and consider a newquantification type which we call soft variable. The idea is toallow a flexible position and quantifier type for these variables.Hence the type of quantifier of the soft variable can also bealtered. Based on this concept, we present an optimization problemseeking an optimal prefix as defined by user-given preferences. We statean algorithm based on MaxQBF, and present several applications – mainlyfrom verification area – which can be naturally translated into theoptimization problem for QBF with soft variables. We further implementeda prototype solver for this formalism, and compare our approach toprevious work, that differently from ours does not guarantee optimalityand completeness

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

論理シミュレーションとハードウェア記述言語に関する研究

京都大学0048新制・論文博士工学博士乙第7496号論工博第2471号新制||工||842(附属図書館)UT51-91-E273(主査)教授 矢島 脩三, 教授 津田 孝夫, 教授 田丸 啓吉学位規則第5条第2項該当Kyoto UniversityDFA

Uma ferramenta simples de balanço de água subterrânea para avaliar o rendimento específico tridimensional e a recarga bidimensional: aplicação a um aqüífero cristalino profundamente intemperizado no sul da Índia

International audienceCrystalline aquifers are among the most complex groundwater systems, requiring adequate methods for realistic characterization and suitable techniques for improving the long-term management of groundwater resources. A tool is needed that can assess the aquifer hydrodynamic parameters cost-effectively. A model is presented, based on a groundwater- budget equation and water-table fluctuation method, which combines the upscaling and the regionalization of aquifer parameters, in particular specific yield (S y) in three dimensions (3D) and the recharge in two dimensions (2-D) from rainfall at watershed scale. The tool was tested and validated on the 53-km 2 Maheshwaram watershed, southern India, at a 685 m × 685 m cell scale, and was calibrated on seasonal groundwater levels from 2011 to 2016. Comparison between computed and observed levels shows an absolute residual mean and a root mean square error of 1.17 m and 1.8 m, respectively, showing the robustness of the model. S y ranges from 0.3 to 5% (mean 1.4%), which is in good agreement with previous studies. The annual recharge from rainfall is also in good agreement with earlier studies and, despite its strong annual variability (16 to 199 mm/y) at watershed scale, it shows that spatial recharge is clearly controlled by spatial structure, from one year to another. Groundwater levels were also forecasted from 2020 to 2039 based on the climate and groundwater abstraction scenarios. The results show severe water-level depletion around 2024-2026 but it would be more stable in the future (after 2030) because of a lower frequency of low-rainfall monsoons

A Multi-Engine Approach to Answer Set Programming

Answer Set Programming (ASP) is a truly-declarative programming paradigm proposed in the area of non-monotonic reasoning and logic programming, that has been recently employed in many applications. The development of efficient ASP systems is, thus, crucial. Having in mind the task of improving the solving methods for ASP, there are two usual ways to reach this goal: $(i)$ extending state-of-the-art techniques and ASP solvers, or $(ii)$ designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of-the-art solvers, and to apply machine learning techniques for choosing automatically the "best" available solver on a per-instance basis. In this paper we pursue this latter direction. We first define a set of cheap-to-compute syntactic features that characterize several aspects of ASP programs. Then, we apply classification methods that, given the features of the instances in a {\sl training} set and the solvers' performance on these instances, inductively learn algorithm selection strategies to be applied to a {\sl test} set. We report the results of a number of experiments considering solvers and different training and test sets of instances taken from the ones submitted to the "System Track" of the 3rd ASP Competition. Our analysis shows that, by applying machine learning techniques to ASP solving, it is possible to obtain very robust performance: our approach can solve more instances compared with any solver that entered the 3rd ASP Competition. (To appear in Theory and Practice of Logic Programming (TPLP).)Comment: 26 pages, 8 figure