ASlib: A Benchmark Library for Algorithm Selection

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa

SAT-based approaches for constraint optimization

La optimització amb restriccions ha estat utilitzada amb èxit par a resoldre problemes en molts dominis reals (industrials). Aquesta tesi es centra en les aproximacions lògiques, concretament en Màxima Satisfactibilitat (MaxSAT) que és la versió d’optimització del problema de Satisfactibilitat booleana (SAT). A través de MaxSAT, s’han resolt molts problemes de forma eficient. Famílies d’instàncies de la majoria d’aquests problemes han estat sotmeses a la MaxSAT Evaluation (MSE), creant així una col•lecció pública i accessible d’instàncies de referència. En les edicions recents de la MSE, els algorismes SAT-based han estat les aproximacions que han tingut un millor comportament per a les instàncies industrials. Aquesta tesi està centrada en millorar els algorismes SAT-based . El nostre treball ha contribuït a tancar varies instàncies obertes i a reduir dramàticament el temps de resolució en moltes altres. A més, hem trobat sorprenentment que reformular y resoldre el problema MaxSAT a través de programació lineal sencera era especialment adequat per algunes famílies. Finalment, hem desenvolupat el primer portfoli altament eficient par a MaxSAT que ha dominat en totes las categories de la MSE des de 2013.La optimización con restricciones ha sido utilizada con éxito para resolver problemas en muchos dominios reales (industriales). Esta tesis se centra en las aproximaciones lógicas, concretamente en Máxima Satisfacibilidad (MaxSAT) que es la versión de optimización del problema de Satisfacibilidad booleana (SAT). A través de MaxSAT, se han resuelto muchos problemas de forma eficiente. Familias de instancias de la mayoría de ellos han sido sometidas a la MaxSAT Evaluation (MSE), creando así una colección pública y accesible de instancias de referencia. En las ediciones recientes de la MSE, los algoritmos SAT-based han sido las aproximaciones que han tenido un mejor comportamiento para las instancias industriales. Esta tesis está centrada en mejorar los algoritmos SAT-based. Nuestro trabajo ha contribuido a cerrar varias instancias abiertas y a reducir dramáticamente el tiempo de resolución en muchas otras. Además, hemos encontrado sorprendentemente que reformular y resolver el problema MaxSAT a través de programación lineal entera era especialmente adecuado para algunas familias. Finalmente, hemos desarrollado el primer portfolio altamente eficiente para MaxSAT que ha dominado en todas las categorías de la MSE desde 2013.Constraint optimization has been successfully used to solve problems in many real world (industrial) domains. This PhD thesis is focused on logic-based approaches, in particular, on Maximum Satisfiability (MaxSAT) which is the optimization version of Satisfiability (SAT). There have been many problems efficiency solved through MaxSAT. Instance families on the majority of them have been submitted to the international MaxSAT Evaluation (MSE), creating a collection of publicly available benchmark instances. At recent editions of MSE, SAT-based algorithms were the best performing single algorithm approaches for industrial problems. This PhD thesis is focused on the improvement of SAT-based algorithms. All this work has contributed to close up some open instances and to reduce dramatically the solving time in many others. In addition, we have surprisingly found that reformulating and solving the MaxSAT problem through Integer Linear Programming (ILP) was extremely well suited for some families. Finally, we have developed the first highly efficient MaxSAT portfolio that dominated all categories of MSE since 2013

sunny-as2: Enhancing SUNNY for Algorithm Selection

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems, and its parallel version won many gold medals in the Open category of the MiniZinc Challenge -- the yearly international competition for CP solvers. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as2, an algorithm selector based on SUNNY for ASlib scenarios. A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work, we present the technical advancements of sunny-as2, including: (i) wrapper-based feature selection; (ii) a training approach combining feature selection and neighbourhood size configuration; (iii) the application of nested cross-validation. We show how sunny-as2 performance varies depending on the considered AS scenarios, and we discuss its strengths and weaknesses. Finally, we also show how sunny-as2 improves on its preliminary version submitted to OASC

Extensions and Experimental Evaluation of SAT-based solvers for the UAQ problem

Nowadays, most of the health organizations make use of Health Information Systems (HIS) to support the staff to provide patients with proper care service. In this context, security and privacy are key to establish trust between the actors involved in the healthcare process, including the patient. However, patients' privacy cannot jeopardize their safety: as a consequence, a compromise between the two must eventually be found. Privilege management and access control are necessary elements to provide security and privacy. In this thesis, we first present the main features that make the Role Based Access Control suitable for permissions management and access control in HIS. We then address the User Authorization Query (UAQ) problem for RBAC, namely the problem of determining the optimum set of roles to activate to provide the user with the requested permissions (if the user is authorized) while satisfying a set of Dynamic Mutually Exclusive Roles (DMER) constraints and achieving some optimization objective (least privilege versus availability). As a first contribution, we show how DMER can be used to support the enforcement of SoD. The UAQ problem is known to be NP-hard. Most of the techniques proposed in the literature to solve it have been experimentally evaluated by running them against different benchmark problems. However, the adequacy of the latter is seldom discussed. In this thesis, we propose a methodology for evaluating existing benchmarks or designing new ones: the methodology leverages the asymptotic complexity analysis of the solving procedures provided in other works to forsee the benchmarks complexity given the values of the most significant RBAC dimensions. First, we use our methodology to demonstrate that the state-of-the-art benchmarks are unsatisfactory. We then introduce UAQ-Solve, a tool that works both as generator of benchmarks and as UAQ solver leveraging existing PMAXSAT complete solvers. By using UAQ-Solve, we apply our methodology to generate a novel suite of parametric benchmarks that allows for the systematic assessment of UAQ solvers over a number of relevant dimensions. These include problems for which no polynomial-time algorithm is known as well as problems for which polynomial-time algorithms do exist. We then execute UAQ-Solve over our benchmarks to compare the performance of different complete and incomplete PMAXSAT solvers

MaxSAT by Improved Instance-Specific Algorithm Configuration

Our objective is to boost the state-of-the-art performance in MaxSATsolving. To this end, we employ the instance-specific algorithmconfigurator ISAC, and improve it with the latest inportfolio technology. Experimental results on SAT show that thiscombination marks a significant step forward in our ability to tunealgorithms instance-specifically. We then apply the new methodology toa number of MaxSAT problem domains and show that the resulting solversconsistently outperform the best existing solvers on the respectiveproblem families. In fact, the solvers presented here were independentlyevaluated at the 2013 MaxSAT Evaluation where they won six of the elevencategories