@misc{AlvianoRomeroDavilaSchaub2018,
  author    = {Alviano, Mario and Romero Davila, Javier and Schaub, Torsten H.},
  title     = {Preference Relations by Approximation},
  series = {Sixteenth International Conference on Principles of Knowledge Representation and Reasoning},
  journal   = {Sixteenth International Conference on Principles of Knowledge Representation and Reasoning},
  publisher = {AAAI Conference on Artificial Intelligence},
  address   = {Palo Alto},
  pages     = {2 -- 11},
  year      = {2018},
  abstract  = {Declarative languages for knowledge representation and reasoning provide constructs to define preference relations over the set of possible interpretations, so that preferred models represent optimal solutions of the encoded problem. We introduce the notion of approximation for replacing preference relations with stronger preference relations, that is, relations comparing more pairs of interpretations. Our aim is to accelerate the computation of a non-empty subset of the optimal solutions by means of highly specialized algorithms. We implement our approach in Answer Set Programming (ASP), where problems involving quantitative and qualitative preference relations can be addressed by ASPRIN, implementing a generic optimization algorithm. Unlike this, chains of approximations allow us to reduce several preference relations to the preference relations associated with ASP's native weak constraints and heuristic directives. In this way, ASPRIN can now take advantage of several highly optimized algorithms implemented by ASP solvers for computing optimal solutions},
  language  = {en}
}
@article{GebserKaminskiKaufmannetal.2018,
  author    = {Gebser, Martin and Kaminski, Roland and Kaufmann, Benjamin and L{\"u}hne, Patrick and Obermeier, Philipp and Ostrowski, Max and Romero Davila, Javier and Schaub, Torsten H. and Schellhorn, Sebastian and Wanko, Philipp},
  title     = {The Potsdam Answer Set Solving Collection 5.0},
  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/s13218-018-0528-x},
  pages     = {181 -- 182},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{DimopoulosGebserLuehneetal.2019,
  author    = {Dimopoulos, Yannis and Gebser, Martin and L{\"u}hne, Patrick and Romero Davila, Javier and Schaub, Torsten H.},
  title     = {plasp 3},
  series = {Theory and practice of logic programming},
  volume    = {19},
  journal   = {Theory and practice of logic programming},
  number    = {3},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068418000583},
  pages     = {477 -- 504},
  year      = {2019},
  abstract  = {We describe the new version of the Planning Domain Definition Language (PDDL)-to-Answer Set Programming (ASP) translator plasp. First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by Satisfiability Testing (SAT) planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.},
  language  = {en}
}