@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}
}
@misc{RazzaqKaminskiRomeroetal.2018,
  author    = {Razzaq, Misbah and Kaminski, Roland and Romero, Javier and Schaub, Torsten H. and Bourdon, Jeremie and Guziolowski, Carito},
  title     = {Computing diverse boolean networks from phosphoproteomic time series data},
  series = {Computational Methods in Systems Biology},
  volume    = {11095},
  journal   = {Computational Methods in Systems Biology},
  publisher = {Springer},
  address   = {Berlin},
  isbn      = {978-3-319-99429-1},
  issn      = {0302-9743},
  doi       = {10.1007/978-3-319-99429-1_4},
  pages     = {59 -- 74},
  year      = {2018},
  abstract  = {Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines.},
  language  = {en}
}
@article{CabalarFandinnoGareaetal.2020,
  author    = {Cabalar, Pedro and Fandinno, Jorge and Garea, Javier and Romero, Javier and Schaub, Torsten H.},
  title     = {Eclingo},
  series = {Theory and practice of logic programming},
  volume    = {20},
  journal   = {Theory and practice of logic programming},
  number    = {6},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068420000228},
  pages     = {834 -- 847},
  year      = {2020},
  abstract  = {We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set Programming system clingo. The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program. The eclingo solving process follows a guess and check strategy. It first generates potential truth values for subjective literals and, in a second step, it checks the obtained result with respect to the cautious and brave consequences of the program. This process is implemented using the multi-shot functionalities of clingo. We have also implemented some optimisations, aiming at reducing the search space and, therefore, increasing eclingo 's efficiency in some scenarios. Finally, we compare the efficiency of eclingo with two state-of-the-art solvers for epistemic logic programs on a pair of benchmark scenarios and show that eclingo generally outperforms their obtained results.},
  language  = {en}
}
@article{FandinnoLaferriereRomeroetal.2021,
  author    = {Fandinno, Jorge and Laferriere, Francois and Romero, Javier and Schaub, Torsten H. and Son, Tran Cao},
  title     = {Planning with incomplete information in quantified answer set programming},
  series = {Theory and practice of logic programming},
  volume    = {21},
  journal   = {Theory and practice of logic programming},
  number    = {5},
  publisher = {Cambridge University Press},
  address   = {Cambridge},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068421000259},
  pages     = {663 -- 679},
  year      = {2021},
  abstract  = {We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.},
  language  = {en}
}