Solving of constraint satisfaction problems by reduction to SAT

Mnogi realni problemi se danas mogu predstaviti u obliku problema zadovoljenja ogranicenja (CSP) i zatim rešiti nekom od mnogobrojnih tehnika za rešavanje ovog problema. Jedna od tehnika podrazumeva svođenje na problem SAT, tj. problem iskazne zadovoljivosti. Promenljive i ogranicenja problema CSP se prevode (kodiraju) u SAT instancu, ona se potom rešava pomocu modernih SAT rešavaca i rešenje se, ako postoji, prevodi u rešenje problema CSP. Glavni cilj ove teze je unapređenje rešavanja problema CSP svođenjem na SAT. Razvijena su dva nova hibridna kodiranja (prevođenja u SAT formulu) koja kombinuju dobre strane postojecih kodiranja. Dat je dokaz korektnosti jednog od kodiranja koji do sada nije postojao u literaturi. Razvijen je sistem meSAT koji omogucava svođenje problema CSP na SAT pomocu cetiri osnovna i dva hibridna kodiranja, kao i rešavanje problema CSP svođenjem na dva problema srodna problemu SAT, SMT i PB. Razvijen je portfolio za automatski odabir kodiranja/rešavaca za ulaznu instancu koju je potrebno rešiti i pokazano je da je razvijeni portfolio uporediv sa najefikasnijim savremenim pristupima. Prikazan je novi pristup zasnovan na kratkim vremenskim ogranicenjima sa ciljem da se znacajno smanji vreme pripreme portfolija. Pokazano je da se ovim pristupom dobijaju rezultati konkurentni onima koji se dobijaju korišcenjem standardnog vremena za pripremu. Izvršeno je poređenje nekoliko tehnika mašinskog ucenja, sa ciljem da se ustanovi koja od njih je pogodnija za pristup sa kratkim treniranjem. Prikazan je jedan realan problem, problem raspoređivanja kontrolora leta, kao i tri njegova modela. Veliki broj metoda rešavanja i raznovrsnih rešavaca je upotrebljeno za rešavanje ovog problema. Razvijeno je više optimizacionih tehnika koje imaju za cilj pronalaženje optimalnih rešenja problema. Pokazuje se da je najefikasnija hibridna tehnika koja kombinuje svođenje na SAT i lokalnu pretragu. Razmotren je i problem sudoku, kao i postojece tehnike rešavanja sudoku zagonetki vecih dimenzija od 9 x 9. Pokazuje se da je u rešavanju ovih zagonetki najefikasnije vec postojece svođenje na SAT. Unapređen je postojeci algoritam za generisanje velikih sudoku zagonetki. Pokazano je da jednostavna pravila preprocesiranja dodatno unapređuju brzinu generisanja sudokua.Many real-world problems can be modeled as constraint satisfaction problems (CSPs) and then solved by one of many available techniques for solving these problems. One of the techniques is reduction to SAT, i.e. Boolean Satisfiability Problem. Variables and constraints of CSP are translated (encoded) to SAT instance, that is then solved by state-of-the-art SAT solvers and solution, if exists, is translated to the solution of the original CSP. The main aim of this thesis is to improve CSP solving techniques that are using reduction to SAT. Two new hybrid encodings of CSPs to SAT are presented and they combine good sides of the existing encodings. We give the proof of correctness of one encoding that did not exist in literature. We developed system meSAT that enables reduction of CSPs to SAT by using 4 basic and 2 hybrid encodings. The system also enables solving of CSPs by reduction to two problems related to SAT, SMT and PB. We developed a portfolio for automated selection of encoding/solver to be used on some new instance that needs to be solved. The developed portfolio is comparable with the state-of-the-art portfolios. We developed a hybrid approach based on short solving timeouts with the aim of significantly reducing the preparation time of a portfolio. By using this approach, we got results comparable to the ones obtained by using preparation time of usual length. We made comparison between several machine learning techniques with the aim to find out which one is the best suited for the short training approach. The problem of assigning air traffic controllers to shifts is described and three models of this problem are presented. We used a large number of different solving methods and a diverse set of solvers for solving this problem. We developed optimization techniques that aim to find optimal solutions of the problem. A hybrid technique combining reduction to SAT and local search is shown to be the most efficient one. We also considered sudoku puzzles and the existing techniques of solving the puzzles of greater size than 9x9. Amongst the used techniques, the existing reduction to SAT is the most efficient in solving these puzzles. We improved the existing algorithm for generating large sudoku puzzles. It is shown that simple preprocessing rules additionally improve speed of generating large sudokus

An Enhanced Features Extractor for a Portfolio of Constraint Solvers

Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specification. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP. We also report some empirical results showing that the performances that can be obtained using these features are effective and competitive with state of the art CSP portfolio techniques

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

Proteus: A Hierarchical Portfolio of Solvers and Transformations

In recent years, portfolio approaches to solving SAT problems and CSPs have become increasingly common. There are also a number of different encodings for representing CSPs as SAT instances. In this paper, we leverage advances in both SAT and CSP solving to present a novel hierarchical portfolio-based approach to CSP solving, which we call Proteus, that does not rely purely on CSP solvers. Instead, it may decide that it is best to encode a CSP problem instance into SAT, selecting an appropriate encoding and a corresponding SAT solver. Our experimental evaluation used an instance of Proteus that involved four CSP solvers, three SAT encodings, and six SAT solvers, evaluated on the most challenging problem instances from the CSP solver competitions, involving global and intensional constraints. We show that significant performance improvements can be achieved by Proteus obtained by exploiting alternative view-points and solvers for combinatorial problem-solving.Comment: 11th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. The final publication is available at link.springer.co

Automated generation of computationally hard feature models using evolutionary algorithms

This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.European Commission (FEDER), the Spanish Government and the Andalusian Government

Fast counting with tensor networks

We introduce tensor network contraction algorithms for counting satisfying assignments of constraint satisfaction problems (#CSPs). We represent each arbitrary #CSP formula as a tensor network, whose full contraction yields the number of satisfying assignments of that formula, and use graph theoretical methods to determine favorable orders of contraction. We employ our heuristics for the solution of #P-hard counting boolean satisfiability (#SAT) problems, namely monotone #1-in-3SAT and #Cubic-Vertex-Cover, and find that they outperform state-of-the-art solvers by a significant margin.Comment: v2: added results for monotone #1-in-3SAT; published versio