Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen OPUS4-46381 Wissenschaftlicher Artikel Janhunen, Tomi; Kaminski, Roland; Ostrowski, Max; Schellhorn, Sebastian; Wanko, Philipp; Schaub, Torsten H. Clingo goes linear constraints over reals and integers The recent series 5 of the Answer Set Programming (ASP) system clingo provides generic means to enhance basic ASP with theory reasoning capabilities. We instantiate this framework with different forms of linear constraints and elaborate upon its formal properties. Given this, we discuss the respective implementations, and present techniques for using these constraints in a reactive context. More precisely, we introduce extensions to clingo with difference and linear constraints over integers and reals, respectively, and realize them in complementary ways. Finally, we empirically evaluate the resulting clingo derivatives clingo[dl] and clingo[lp] on common language fragments and contrast them to related ASP systems. New York Cambridge Univ. Press 2017 17 Theory and practice of logic programming 17 872 888 10.1017/S1471068417000242 OPUS4-52386 Wissenschaftlicher Artikel Gebser, Martin; Kaminski, Roland; Kaufmann, Benjamin; Lühne, Patrick; Obermeier, Philipp; Ostrowski, Max; Romero Davila, Javier; Schaub, Torsten H.; Schellhorn, Sebastian; Wanko, Philipp The Potsdam Answer Set Solving Collection 5.0 The Potsdam answer set solving collection, or Potassco for short, bundles various tools implementing and/or applying answer set programming. The article at hand succeeds an earlier description of the Potassco project published in Gebser et al. (AI Commun 24(2):107-124, 2011). Hence, we concentrate in what follows on the major features of the most recent, fifth generation of the ASP system clingo and highlight some recent resulting application systems. Heidelberg Springer 2018 2 Künstliche Intelligenz 32 2-3 181 182 10.1007/s13218-018-0528-x Institut für Informatik und Computational Science OPUS4-36760 Wissenschaftlicher Artikel Durzinsky, Markus; Marwan, Wolfgang; Ostrowski, Max; Schaub, Torsten H.; Wagler, Annegret Automatic network reconstruction using ASP Building biological models by inferring functional dependencies from experimental data is an important issue in Molecular Biology. To relieve the biologist from this traditionally manual process, various approaches have been proposed to increase the degree of automation. However, available approaches often yield a single model only, rely on specific assumptions, and/or use dedicated, heuristic algorithms that are intolerant to changing circumstances or requirements in the view of the rapid progress made in Biotechnology. Our aim is to provide a declarative solution to the problem by appeal to Answer Set Programming (ASP) overcoming these difficulties. We build upon an existing approach to Automatic Network Reconstruction proposed by part of the authors. This approach has firm mathematical foundations and is well suited for ASP due to its combinatorial flavor providing a characterization of all models explaining a set of experiments. The usage of ASP has several benefits over the existing heuristic algorithms. First, it is declarative and thus transparent for biological experts. Second, it is elaboration tolerant and thus allows for an easy exploration and incorporation of biological constraints. Third, it allows for exploring the entire space of possible models. Finally, our approach offers an excellent performance, matching existing, special-purpose systems. New York Cambridge Univ. Press 2011 18 Theory and practice of logic programming 11 749 766 10.1017/S1471068411000287 Institut für Informatik und Computational Science OPUS4-35775 Wissenschaftlicher Artikel Ostrowski, Max; Schaub, Torsten H. ASP modulo CSP The clingcon system We present the hybrid ASP solver clingcon, combining the simple modeling language and the high performance Boolean solving capacities of Answer Set Programming (ASP) with techniques for using non-Boolean constraints from the area of Constraint Programming (CP). The new clingcon system features an extended syntax supporting global constraints and optimize statements for constraint variables. The major technical innovation improves the interaction between ASP and CP solver through elaborated learning techniques based on irreducible inconsistent sets. A broad empirical evaluation shows that these techniques yield a performance improvement of an order of magnitude. New York Cambridge Univ. Press 2012 19 Theory and practice of logic programming 12 485 503 10.1017/S1471068412000142 Institut für Informatik und Computational Science OPUS4-37128 Wissenschaftlicher Artikel Gebser, Martin; Kaufmann, Benjamin; Kaminski, Roland; Ostrowski, Max; Schaub, Torsten H.; Schneider, Marius Potassco the Potsdam answer set solving collection This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University of Potsdam. Amsterdam IOS Press 2011 18 AI communications : AICOM ; the European journal on artificial intelligence 24 2 107 124 10.3233/AIC-2011-0491 Institut für Informatik und Computational Science OPUS4-40779 Dissertation Ostrowski, Max Modern constraint answer set solving Answer Set Programming (ASP) is a declarative problem solving approach, combining a rich yet simple modeling language with high-performance solving capabilities. Although this has already resulted in various applications, certain aspects of such applications are more naturally modeled using variables over finite domains, for accounting for resources, fine timings, coordinates, or functions. Our goal is thus to extend ASP with constraints over integers while preserving its declarative nature. This allows for fast prototyping and elaboration tolerant problem descriptions of resource related applications. The resulting paradigm is called Constraint Answer Set Programming (CASP). We present three different approaches for solving CASP problems. The first one, a lazy, modular approach combines an ASP solver with an external system for handling constraints. This approach has the advantage that two state of the art technologies work hand in hand to solve the problem, each concentrating on its part of the problem. The drawback is that inter-constraint dependencies cannot be communicated back to the ASP solver, impeding its learning algorithm. The second approach translates all constraints to ASP. Using the appropriate encoding techniques, this results in a very fast, monolithic system. Unfortunately, due to the large, explicit representation of constraints and variables, translation techniques are restricted to small and mid-sized domains. The third approach merges the lazy and the translational approach, combining the strength of both while removing their weaknesses. To this end, we enhance the dedicated learning techniques of an ASP solver with the inferences of the translating approach in a lazy way. That is, the important knowledge is only made explicit when needed. By using state of the art techniques from neighboring fields, we provide ways to tackle real world, industrial size problems. By extending CASP to reactive solving, we open up new application areas such as online planning with continuous domains and durations. 2018 135 urn:nbn:de:kobv:517-opus4-407799 Institut für Informatik und Computational Science OPUS4-44772 Wissenschaftlicher Artikel Ostrowski, Max; Pauleve, L.; Schaub, Torsten H.; Siegel, A.; Guziolowski, Carito Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7 min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. (C) 2016 Elsevier Ireland Ltd. All rights reserved. Oxford Elsevier 2016 15 Biosystems : journal of biological and information processing sciences 149 139 153 10.1016/j.biosystems.2016.07.009 OPUS4-41390 misc Ostrowski, Max; Schaub, Torsten H. ASP modulo CSP We present the hybrid ASP solver clingcon, combining the simple modeling language and the high performance Boolean solving capacities of Answer Set Programming (ASP) with techniques for using non-Boolean constraints from the area of Constraint Programming (CP). The new clingcon system features an extended syntax supporting global constraints and optimize statements for constraint variables. The major technical innovation improves the interaction between ASP and CP solver through elaborated learning techniques based on irreducible inconsistent sets. A broad empirical evaluation shows that these techniques yield a performance improvement of an order of magnitude. 2012 19 Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe 579 urn:nbn:de:kobv:517-opus4-413908 10.25932/publishup-41390 Mathematisch-Naturwissenschaftliche Fakultät OPUS4-46556 Wissenschaftlicher Artikel Banbara, Mutsunori; Kaufmann, Benjamin; Ostrowski, Max; Schaub, Torsten H. Clingcon: The next generation New York Cambridge Univ. Press 2017 54 Theory and practice of logic programming 17 408 461 10.1017/S1471068417000138 OPUS4-41241 misc Durzinsky, Markus; Marwan, Wolfgang; Ostrowski, Max; Schaub, Torsten H.; Wagler, Annegret Automatic network reconstruction using ASP Building biological models by inferring functional dependencies from experimental data is an important issue in Molecular Biology. To relieve the biologist from this traditionally manual process, various approaches have been proposed to increase the degree of automation. However, available approaches often yield a single model only, rely on specific assumptions, and/or use dedicated, heuristic algorithms that are intolerant to changing circumstances or requirements in the view of the rapid progress made in Biotechnology. Our aim is to provide a declarative solution to the problem by appeal to Answer Set Programming (ASP) overcoming these difficulties. We build upon an existing approach to Automatic Network Reconstruction proposed by part of the authors. This approach has firm mathematical foundations and is well suited for ASP due to its combinatorial flavor providing a characterization of all models explaining a set of experiments. The usage of ASP has several benefits over the existing heuristic algorithms. First, it is declarative and thus transparent for biological experts. Second, it is elaboration tolerant and thus allows for an easy exploration and incorporation of biological constraints. Third, it allows for exploring the entire space of possible models. Finally, our approach offers an excellent performance, matching existing, special-purpose systems. 2011 18 Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe 560 urn:nbn:de:kobv:517-opus4-412419 10.25932/publishup-41241 Mathematisch-Naturwissenschaftliche Fakultät OPUS4-52389 Wissenschaftlicher Artikel Brewka, Gerhard; Ellmauthaler, Stefan; Kern-Isberner, Gabriele; Obermeier, Philipp; Ostrowski, Max; Romero, Javier; Schaub, Torsten H.; Schieweck, Steffen Advanced solving technology for dynamic and reactive applications Heidelberg Springer 2018 2 Künstliche Intelligenz 32 2-3 199 200 10.1007/s13218-018-0538-8 Institut für Informatik und Computational Science