@article{JanhunenKaminskiOstrowskietal.2017, author = {Janhunen, Tomi and Kaminski, Roland and Ostrowski, Max and Schellhorn, Sebastian and Wanko, Philipp and Schaub, Torsten H.}, title = {Clingo goes linear constraints over reals and integers}, series = {Theory and practice of logic programming}, volume = {17}, journal = {Theory and practice of logic programming}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068417000242}, pages = {872 -- 888}, year = {2017}, abstract = {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.}, language = {en} } @article{GebserKaminskiKaufmannetal.2018, author = {Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and L{\"u}hne, Patrick and Obermeier, Philipp and Ostrowski, Max and Romero Davila, Javier and Schaub, Torsten H. and Schellhorn, Sebastian and Wanko, Philipp}, title = {The Potsdam Answer Set Solving Collection 5.0}, series = {K{\"u}nstliche Intelligenz}, volume = {32}, journal = {K{\"u}nstliche Intelligenz}, number = {2-3}, publisher = {Springer}, address = {Heidelberg}, issn = {0933-1875}, doi = {10.1007/s13218-018-0528-x}, pages = {181 -- 182}, year = {2018}, abstract = {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.}, language = {en} } @article{DurzinskyMarwanOstrowskietal.2011, author = {Durzinsky, Markus and Marwan, Wolfgang and Ostrowski, Max and Schaub, Torsten H. and Wagler, Annegret}, title = {Automatic network reconstruction using ASP}, series = {Theory and practice of logic programming}, volume = {11}, journal = {Theory and practice of logic programming}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068411000287}, pages = {749 -- 766}, year = {2011}, abstract = {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.}, language = {en} } @article{OstrowskiSchaub2012, author = {Ostrowski, Max and Schaub, Torsten H.}, title = {ASP modulo CSP The clingcon system}, series = {Theory and practice of logic programming}, volume = {12}, journal = {Theory and practice of logic programming}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068412000142}, pages = {485 -- 503}, year = {2012}, abstract = {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.}, language = {en} } @article{GebserKaufmannKaminskietal.2011, author = {Gebser, Martin and Kaufmann, Benjamin and Kaminski, Roland and Ostrowski, Max and Schaub, Torsten H. and Schneider, Marius}, title = {Potassco the Potsdam answer set solving collection}, series = {AI communications : AICOM ; the European journal on artificial intelligence}, volume = {24}, journal = {AI communications : AICOM ; the European journal on artificial intelligence}, number = {2}, publisher = {IOS Press}, address = {Amsterdam}, issn = {0921-7126}, doi = {10.3233/AIC-2011-0491}, pages = {107 -- 124}, year = {2011}, abstract = {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.}, language = {en} } @phdthesis{Ostrowski2018, author = {Ostrowski, Max}, title = {Modern constraint answer set solving}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407799}, school = {Universit{\"a}t Potsdam}, pages = {135}, year = {2018}, abstract = {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.}, language = {en} } @article{OstrowskiPauleveSchaubetal.2016, author = {Ostrowski, Max and Pauleve, L. and Schaub, Torsten H. and Siegel, A. and Guziolowski, Carito}, title = {Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming}, series = {Biosystems : journal of biological and information processing sciences}, volume = {149}, journal = {Biosystems : journal of biological and information processing sciences}, publisher = {Elsevier}, address = {Oxford}, issn = {0303-2647}, doi = {10.1016/j.biosystems.2016.07.009}, pages = {139 -- 153}, year = {2016}, abstract = {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.}, language = {en} } @misc{OstrowskiSchaub2012, author = {Ostrowski, Max and Schaub, Torsten H.}, title = {ASP modulo CSP}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {579}, issn = {1866-8372}, doi = {10.25932/publishup-41390}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-413908}, pages = {19}, year = {2012}, abstract = {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.}, language = {en} } @article{BanbaraKaufmannOstrowskietal.2017, author = {Banbara, Mutsunori and Kaufmann, Benjamin and Ostrowski, Max and Schaub, Torsten H.}, title = {Clingcon: The next generation}, series = {Theory and practice of logic programming}, volume = {17}, journal = {Theory and practice of logic programming}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068417000138}, pages = {408 -- 461}, year = {2017}, language = {en} } @misc{DurzinskyMarwanOstrowskietal.2011, author = {Durzinsky, Markus and Marwan, Wolfgang and Ostrowski, Max and Schaub, Torsten H. and Wagler, Annegret}, title = {Automatic network reconstruction using ASP}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {560}, issn = {1866-8372}, doi = {10.25932/publishup-41241}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-412419}, pages = {18}, year = {2011}, abstract = {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.}, language = {en} } @article{BrewkaEllmauthalerKernIsberneretal.2018, author = {Brewka, Gerhard and Ellmauthaler, Stefan and Kern-Isberner, Gabriele and Obermeier, Philipp and Ostrowski, Max and Romero, Javier and Schaub, Torsten H. and Schieweck, Steffen}, title = {Advanced solving technology for dynamic and reactive applications}, series = {K{\"u}nstliche Intelligenz}, volume = {32}, journal = {K{\"u}nstliche Intelligenz}, number = {2-3}, publisher = {Springer}, address = {Heidelberg}, issn = {0933-1875}, doi = {10.1007/s13218-018-0538-8}, pages = {199 -- 200}, year = {2018}, language = {en} }