@article{GebserKaufmannSchaub2012,
  author    = {Gebser, Martin and Kaufmann, Benjamin and Schaub, Torsten H.},
  title     = {Conflict-driven answer set solving: From theory to practice},
  series = {Artificial intelligence},
  volume    = {187},
  journal   = {Artificial intelligence},
  number    = {8},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0004-3702},
  doi       = {10.1016/j.artint.2012.04.001},
  pages     = {52 -- 89},
  year      = {2012},
  abstract  = {We introduce an approach to computing answer sets of logic programs, based on concepts successfully applied in Satisfiability (SAT) checking. The idea is to view inferences in Answer Set Programming (ASP) as unit propagation on nogoods. This provides us with a uniform constraint-based framework capturing diverse inferences encountered in ASP solving. Moreover, our approach allows us to apply advanced solving techniques from the area of SAT. As a result, we present the first full-fledged algorithmic framework for native conflict-driven ASP solving. Our approach is implemented in the ASP solver clasp that has demonstrated its competitiveness and versatility by winning first places at various solver contests.},
  language  = {en}
}
@article{VidelaGuziolowskiEduatietal.2015,
  author    = {Videla, Santiago and Guziolowski, Carito and Eduati, Federica and Thiele, Sven and Gebser, Martin and Nicolas, Jacques and Saez-Rodriguez, Julio and Schaub, Torsten H. and Siegel, Anne},
  title     = {Learning Boolean logic models of signaling networks with ASP},
  series = {Theoretical computer science},
  volume    = {599},
  journal   = {Theoretical computer science},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3975},
  doi       = {10.1016/j.tcs.2014.06.022},
  pages     = {79 -- 101},
  year      = {2015},
  abstract  = {Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A-B. (C) 2014 Elsevier B.V. All rights reserved.},
  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}
}