@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 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}
}
@misc{LifschitzSchaubWoltran2018,
  author    = {Lifschitz, Vladimir and Schaub, Torsten and Woltran, Stefan},
  title     = {Interview with Vladimir Lifschitz},
  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/s13218-018-0552-x},
  pages     = {213 -- 218},
  year      = {2018},
  abstract  = {This interview with Vladimir Lifschitz was conducted by Torsten Schaub at the University of Texas at Austin in August 2017. The question set was compiled by Torsten Schaub and Stefan Woltran.},
  language  = {en}
}
@phdthesis{Hecher2021,
  author    = {Hecher, Markus},
  title     = {Advanced tools and methods for treewidth-based problem solving},
  doi       = {10.25932/publishup-51251},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-512519},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xv, 184},
  year      = {2021},
  abstract  = {In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver's interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth. In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth. Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.},
  language  = {en}
}