TY - JOUR A1 - Haubelt, Christian A1 - Neubauer, Kai A1 - Schaub, Torsten A1 - Wanko, Philipp T1 - Design space exploration with answer set programming JF - Künstliche Intelligenz N2 - The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE. Y1 - 2018 U6 - https://doi.org/10.1007/s13218-018-0530-3 SN - 0933-1875 SN - 1610-1987 VL - 32 IS - 2-3 SP - 205 EP - 206 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 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 - Frioux, Clémence A1 - Schaub, Torsten A1 - Schellhorn, Sebastian A1 - Siegel, Anne A1 - Wanko, Philipp T1 - Hybrid metabolic network completion JF - Theory and practice of logic programming N2 - Metabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an answer set programming (ASP)-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system dingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli. Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches. KW - answer set programming KW - metabolic network KW - gap-filling KW - linear programming KW - hybrid solving KW - bioinformatics Y1 - 2018 U6 - https://doi.org/10.1017/S1471068418000455 SN - 1471-0684 SN - 1475-3081 VL - 19 IS - 1 SP - 83 EP - 108 PB - Cambridge University Press CY - New York ER - TY - JOUR A1 - Banbara, Mutsunori A1 - Inoue, Katsumi A1 - Kaufmann, Benjamin A1 - Okimoto, Tenda A1 - Schaub, Torsten A1 - Soh, Takehide A1 - Tamura, Naoyuki A1 - Wanko, Philipp T1 - teaspoon BT - solving the curriculum-based course timetabling problems with answer set programming JF - Annals of operation research N2 - Answer Set Programming (ASP) is an approach to declarative problem solving, combining a rich yet simple modeling language with high performance solving capacities. We here develop an ASP-based approach to curriculum-based course timetabling (CB-CTT), one of the most widely studied course timetabling problems. The resulting teaspoon system reads a CB-CTT instance of a standard input format and converts it into a set of ASP facts. In turn, these facts are combined with a first-order encoding for CB-CTT solving, which can subsequently be solved by any off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting it to the best known bounds obtained so far via dedicated implementations. Furthermore, we extend the teaspoon system to multi-objective course timetabling and consider minimal perturbation problems. KW - Educational timetabling KW - Course timetabling KW - Answer set programming KW - Multi-objective optimization KW - Minimal perturbation problems Y1 - 2018 U6 - https://doi.org/10.1007/s10479-018-2757-7 SN - 0254-5330 SN - 1572-9338 VL - 275 IS - 1 SP - 3 EP - 37 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Janhunen, Tomi A1 - Kaminski, Roland A1 - Ostrowski, Max A1 - Schellhorn, Sebastian A1 - Wanko, Philipp A1 - Schaub, Torsten 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 -