@misc{GebserKaminskiSchaub2011,
  author    = {Gebser, Martin and Kaminski, Roland and Schaub, Torsten H.},
  title     = {Complex optimization in answer set programming},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {554},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41243},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-412436},
  pages     = {19},
  year      = {2011},
  abstract  = {Preference handling and optimization are indispensable means for addressing nontrivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, like saturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-prpogramming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications.},
  language  = {en}
}
@misc{GebserSchaubThieleetal.2011,
  author    = {Gebser, Martin and Schaub, Torsten H. and Thiele, Sven and Veber, Philippe},
  title     = {Detecting inconsistencies in large biological networks with answer set programming},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {561},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41246},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-412467},
  pages     = {38},
  year      = {2011},
  abstract  = {We introduce an approach to detecting inconsistencies in large biological networks by using answer set programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on answer set programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.},
  language  = {en}
}