TY - JOUR A1 - Brewka, Gerhard A1 - Ellmauthaler, Stefan A1 - Kern-Isberner, Gabriele A1 - Obermeier, Philipp A1 - Ostrowski, Max A1 - Romero, Javier A1 - Schaub, Torsten H. A1 - Schieweck, Steffen T1 - Advanced solving technology for dynamic and reactive applications JF - Künstliche Intelligenz Y1 - 2018 U6 - https://doi.org/10.1007/s13218-018-0538-8 SN - 0933-1875 SN - 1610-1987 VL - 32 IS - 2-3 SP - 199 EP - 200 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Gebser, Martin A1 - Kaminski, Roland A1 - Kaufmann, Benjamin A1 - Lühne, Patrick A1 - Obermeier, Philipp A1 - Ostrowski, Max A1 - Romero Davila, Javier A1 - Schaub, Torsten H. A1 - Schellhorn, Sebastian A1 - Wanko, Philipp T1 - The Potsdam Answer Set Solving Collection 5.0 JF - Künstliche Intelligenz N2 - 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. Y1 - 2018 U6 - https://doi.org/10.1007/s13218-018-0528-x SN - 0933-1875 SN - 1610-1987 VL - 32 IS - 2-3 SP - 181 EP - 182 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Banbara, Mutsunori A1 - Kaufmann, Benjamin A1 - Ostrowski, Max A1 - Schaub, Torsten H. T1 - Clingcon: The next generation JF - Theory and practice of logic programming KW - Constraint Answer Set Programming (CASP) KW - Answer Set Programming (ASP) KW - Sat Modulo Theories (SMT) KW - Constraint Programming (CP) Y1 - 2017 U6 - https://doi.org/10.1017/S1471068417000138 SN - 1471-0684 SN - 1475-3081 VL - 17 SP - 408 EP - 461 PB - Cambridge Univ. Press CY - New York ER - TY - JOUR A1 - Janhunen, Tomi A1 - Kaminski, Roland A1 - Ostrowski, Max A1 - Schellhorn, Sebastian A1 - Wanko, Philipp A1 - Schaub, Torsten H. T1 - Clingo goes linear constraints over reals and integers JF - Theory and practice of logic programming N2 - 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. KW - Constraint Answer Set Programming (CASP) KW - Answer Set Programming (ASP) KW - Constraint Processing (CP) KW - Theory Solving Y1 - 2017 U6 - https://doi.org/10.1017/S1471068417000242 SN - 1471-0684 SN - 1475-3081 VL - 17 SP - 872 EP - 888 PB - Cambridge Univ. Press CY - New York ER - TY - JOUR A1 - Ostrowski, Max A1 - Pauleve, L. A1 - Schaub, Torsten H. A1 - Siegel, A. A1 - Guziolowski, Carito T1 - Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming JF - Biosystems : journal of biological and information processing sciences N2 - 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. KW - Model identification KW - Time series data KW - Multiplex phosphoproteomics data KW - Boolean networks KW - Answer Set Programming Y1 - 2016 U6 - https://doi.org/10.1016/j.biosystems.2016.07.009 SN - 0303-2647 SN - 1872-8324 VL - 149 SP - 139 EP - 153 PB - Elsevier CY - Oxford ER - TY - GEN A1 - Ostrowski, Max A1 - Schaub, Torsten H. T1 - ASP modulo CSP BT - the clingcon system T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 579 KW - answer set KW - constraints KW - logic KW - SMT Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-413908 SN - 1866-8372 IS - 579 ER - TY - GEN A1 - Durzinsky, Markus A1 - Marwan, Wolfgang A1 - Ostrowski, Max A1 - Schaub, Torsten H. A1 - Wagler, Annegret T1 - Automatic network reconstruction using ASP T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 560 KW - regulatory networks KW - biological networks KW - answer Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-412419 SN - 1866-8372 IS - 560 ER - TY - THES A1 - Ostrowski, Max T1 - Modern constraint answer set solving T1 - Moderne Constraint Antwortmengenprogrammierung N2 - 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. N2 - Die Antwortmengenprogrammierung (ASP) ist ein deklarativer Ansatz zur Problemlösung. Eine ausdrucksstarke Modellierungssprache erlaubt es, Probleme einfach und flexibel zu beschreiben. Durch sehr effiziente Problemlösungstechniken, konnten bereits verschiedene Anwendungsgebiete erschlossen werden. Allerdings lassen sich Probleme mit Ressourcen besser mit Gleichungen über Ganze oder Reelle Zahlen lösen, anstatt mit reiner Boolescher Logik. In dieser Arbeit erweitern wir ASP mit Arithmetik über Ganze Zahlen zu Constraint Answer Set Programming (CASP). Unser Hauptaugenmerk liegt dabei auf der Erweiterung der Modellierungssprache mit Arithmetik, ohne Performanz oder Flexibilität einzubüßen. In einem ersten, bedarfsgesteuertem, modularen Ansatz kombinieren wir einen ASP Solver mit einem externen System zur Lösung von ganzzahligen Gleichungen. Der Vorteil dieses Ansatzes besteht darin, dass zwei verschiedene Technologien Hand in Hand arbeiten, wobei jede nur ihren Teil des Problems betrachten muss. Ein Nachteil der sich daraus ergibt ist jedoch, dass Abhängigkeiten zwischen den Gleichungen nicht an den ASP Solver kommuniziert werden können. Das beeinträchtigt die Lernfähigkeit des zu Grunde liegenden Algorithmus. Der zweite von uns verfolgte Ansatz übersetzt die ganzzahligen Gleichungen direkt nach ASP. Durch entsprechende Kodierungstechniken erhält man ein sehr effizientes, monolithisches System. Diese Übersetzung erfordert eine explizite Darstellung aller Variablen und Gleichungen. Daher ist dieser Ansatz nur für kleine bis mittlere Wertebereiche geeignet. Die dritte Methode, die wir in dieser Arbeit vorstellen, vereinigt die Vorteile der beiden vorherigen Ansätze und überwindet ihre Kehrseiten. Wir entwickeln einen lernenden Algorithmus, der die Arithmetik implizit lässt. Dies befreit uns davon, eine möglicherweise riesige Menge an Variablen und Formeln zu speichern, und erlaubt es uns gleichzeitig dieses Wissen zu nutzen. Das Ziel dieser Arbeit ist es, durch die Kombination hochmoderner Technologien, industrielle Anwendungsgebiete für ASP zu erschliessen. Die verwendeten Techniken erlauben eine Erweiterung von CASP mit reaktiven Elementen. Das heißt, dass das Lösen des Problems ein interaktiver Prozess wird. Das Problem kann dabei ständig verändert und erweitert werden, ohne dass Informationen verloren gehen oder neu berechnet werden müssen. Dies eröffnet uns neue Möglichkeiten, wie zum Beispiel reaktives Planen mit Ressourcen und Zeiten. KW - ASP (Answer Set Programming) KW - CASP (Constraint Answer Set Programming) KW - constraints KW - hybrid KW - SMT (SAT Modulo Theories) KW - Antwortmengenprogrammierung KW - hybrides Problemlösen Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-407799 ER - TY - JOUR A1 - Gebser, Martin A1 - Kaufmann, Benjamin A1 - Kaminski, Roland A1 - Ostrowski, Max A1 - Schaub, Torsten H. A1 - Schneider, Marius T1 - Potassco the Potsdam answer set solving collection JF - AI communications : AICOM ; the European journal on artificial intelligence N2 - 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. KW - Answer set programming KW - declarative problem solving Y1 - 2011 U6 - https://doi.org/10.3233/AIC-2011-0491 SN - 0921-7126 VL - 24 IS - 2 SP - 107 EP - 124 PB - IOS Press CY - Amsterdam ER - TY - JOUR A1 - Durzinsky, Markus A1 - Marwan, Wolfgang A1 - Ostrowski, Max A1 - Schaub, Torsten H. A1 - Wagler, Annegret T1 - Automatic network reconstruction using ASP JF - Theory and practice of logic programming N2 - 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. Y1 - 2011 U6 - https://doi.org/10.1017/S1471068411000287 SN - 1471-0684 VL - 11 SP - 749 EP - 766 PB - Cambridge Univ. Press CY - New York ER -