@article{GebserSabuncuSchaub2011,
  author    = {Gebser, Martin and Sabuncu, Orkunt and Schaub, Torsten H.},
  title     = {An incremental answer set programming based system for finite model computation},
  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-2011-0496},
  pages     = {195 -- 212},
  year      = {2011},
  abstract  = {We address the problem of Finite Model Computation (FMC) of first-order theories and show that FMC can efficiently and transparently be solved by taking advantage of a recent extension of Answer Set Programming (ASP), called incremental Answer Set Programming (iASP). The idea is to use the incremental parameter in iASP programs to account for the domain size of a model. The FMC problem is then successively addressed for increasing domain sizes until an answer set, representing a finite model of the original first-order theory, is found. We implemented a system based on the iASP solver iClingo and demonstrate its competitiveness by showing that it slightly outperforms the winner of the FNT division of CADE's 2009 Automated Theorem Proving (ATP) competition on the respective benchmark collection.},
  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{GebserKaminskiSchaub2011,
  author    = {Gebser, Martin and Kaminski, Roland and Schaub, Torsten H.},
  title     = {Complex optimization in answer set programming},
  series = {Theory and practice of logic programming},
  volume    = {11},
  journal   = {Theory and practice of logic programming},
  number    = {3},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068411000329},
  pages     = {821 -- 839},
  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}
}
@article{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 = {Theory and practice of logic programming},
  volume    = {11},
  journal   = {Theory and practice of logic programming},
  number    = {5-6},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068410000554},
  pages     = {323 -- 360},
  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}
}
@article{MileoSchaubMericoetal.2011,
  author    = {Mileo, Alessandra and Schaub, Torsten H. and Merico, Davide and Bisiani, Roberto},
  title     = {Knowledge-based multi-criteria optimization to support indoor positioning},
  series = {Annals of mathematics and artificial intelligence},
  volume    = {62},
  journal   = {Annals of mathematics and artificial intelligence},
  number    = {3-4},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1012-2443},
  doi       = {10.1007/s10472-011-9241-2},
  pages     = {345 -- 370},
  year      = {2011},
  abstract  = {Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting.},
  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}
}