Recursive Online Enumeration of All Minimal Unsatisfiable Subsets

In various areas of computer science, we deal with a set of constraints to be satisfied. If the constraints cannot be satisfied simultaneously, it is desirable to identify the core problems among them. Such cores are called minimal unsatisfiable subsets (MUSes). The more MUSes are identified, the more information about the conflicts among the constraints is obtained. However, a full enumeration of all MUSes is in general intractable due to the large number (even exponential) of possible conflicts. Moreover, to identify MUSes algorithms must test sets of constraints for their simultaneous satisfiabilty. The type of the test depends on the application domains. The complexity of tests can be extremely high especially for domains like temporal logics, model checking, or SMT. In this paper, we propose a recursive algorithm that identifies MUSes in an online manner (i.e., one by one) and can be terminated at any time. The key feature of our algorithm is that it minimizes the number of satisfiability tests and thus speeds up the computation. The algorithm is applicable to an arbitrary constraint domain and its effectiveness demonstrates itself especially in domains with expensive satisfiability checks. We benchmark our algorithm against state of the art algorithm on Boolean and SMT constraint domains and demonstrate that our algorithm really requires less satisfiability tests and consequently finds more MUSes in given time limits

Incrementally Computing Minimal Unsatisfiable Cores of QBFs via a Clause Group Solver API

We consider the incremental computation of minimal unsatisfiable cores (MUCs) of QBFs. To this end, we equipped our incremental QBF solver DepQBF with a novel API to allow for incremental solving based on clause groups. A clause group is a set of clauses which is incrementally added to or removed from a previously solved QBF. Our implementation of the novel API is related to incremental SAT solving based on selector variables and assumptions. However, the API entirely hides selector variables and assumptions from the user, which facilitates the integration of DepQBF in other tools. We present implementation details and, for the first time, report on experiments related to the computation of MUCs of QBFs using DepQBF's novel clause group API.Comment: (fixed typo), camera-ready version, 6-page tool paper, to appear in proceedings of SAT 2015, LNCS, Springe

The Multi-engine ASP Solver ME-ASP: Progress Report

MEASP is a multi-engine solver for ground ASP programs. It exploits algorithm selection techniques based on classification to select one among a set of out-of-the-box heterogeneous ASP solvers used as black-box engines. In this paper we report on (i) a new optimized implementation of MEASP; and (ii) an attempt of applying algorithm selection to non-ground programs. An experimental analysis reported in the paper shows that (i) the new implementation of \measp is substantially faster than the previous version; and (ii) the multi-engine recipe can be applied to the evaluation of non-ground programs with some benefits

Towards the Automatic Classification of Documents in User-generated Classifications

There is a huge amount of information scattered on the World Wide Web. As the information flow occurs at a high speed in the WWW, there is a need to organize it in the right manner so that a user can access it very easily. Previously the organization of information was generally done manually, by matching the document contents to some pre-defined categories. There are two approaches for this text-based categorization: manual and automatic. In the manual approach, a human expert performs the classification task, and in the second case supervised classifiers are used to automatically classify resources. In a supervised classification, manual interaction is required to create some training data before the automatic classification task takes place. In our new approach, we intend to propose automatic classification of documents through semantic keywords and building the formulas generation by these keywords. Thus we can reduce this human participation by combining the knowledge of a given classification and the knowledge extracted from the data. The main focus of this PhD thesis, supervised by Prof. Fausto Giunchiglia, is the automatic classification of documents into user-generated classifications. The key benefits foreseen from this automatic document classification is not only related to search engines, but also to many other fields like, document organization, text filtering, semantic index managing

MAMUD : contribution of HR satellite imagery to a better monitoring, modeling and understanding of urban dynamics

In this treatise the discussion of a methodology and results of semi-automatic city DSM extrac-tion from an Ikonos triplet, is introduced. Built-up areas are known as being complex for photogrammetric purposes, partly because of the steep changes in elevation caused by buildings and urban features. To make DSM extraction more robust and to cope with the specific problems of height displacement, concealed areas and shadow, a multi-image based approach is followed. For the VHR tri-stereoscopic study an area extending from the centre of Istanbul to the urban fringe is chosen. Research will concentrate, in first phase on the development of methods to optimize the extraction of photogrammetric products from the bundled Ikonos triplet. Optimal methods need to be found to improve the radiometry and geometry of the imagery, to improve the semi-automatically derivation of DSM’s and to improve the postprocessing of the products. Secondly we will also investigate the possibilities of creating stereo models out of images from the same sensor taken on a different date, e.g. one image of the stereo pair combined with the third image. Finally the photogrammetric products derived from the Ikonos stereo pair as well as the products created out of the triplet and the constructed stereo models will be investigated by comparison with a 3D reference. This evaluation should show the increase of accuracy when multi-imagery is used instead of stereo pairs

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering