@article{SchaubWoltran2018,
  author    = {Schaub, Torsten H. and Woltran, Stefan},
  title     = {Answer set programming unleashed!},
  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/s13218-018-0550-z},
  pages     = {105 -- 108},
  year      = {2018},
  abstract  = {Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92-103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as answer sets, each being a set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem's specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP's modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53-68, 2016; Falkner et al. Industrial applications of answer set programming. K++nstliche Intelligenz (2018)]},
  language  = {en}
}
@misc{SchaubWoltran2018,
  author    = {Schaub, Torsten H. and Woltran, Stefan},
  title     = {Special issue on answer set programming},
  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/s13218-018-0554-8},
  pages     = {101 -- 103},
  year      = {2018},
  language  = {en}
}
@misc{LifschitzSchaubWoltran2018,
  author    = {Lifschitz, Vladimir and Schaub, Torsten H. 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}
}
@misc{BrewkaSchaubWoltran2018,
  author    = {Brewka, Gerhard and Schaub, Torsten H. and Woltran, Stefan},
  title     = {Interview with Gerhard Brewka},
  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/s13218-018-0549-5},
  pages     = {219 -- 221},
  year      = {2018},
  abstract  = {This interview with Gerhard Brewka was conducted by correspondance in May 2018. The question set was compiled by Torsten Schaub and Stefan Woltran.},
  language  = {en}
}