@article{GebserSchaub2016,
  author    = {Gebser, Martin and Schaub, Torsten H.},
  title     = {Modeling and Language Extensions},
  series = {AI magazine},
  volume    = {37},
  journal   = {AI magazine},
  publisher = {Association for the Advancement of Artificial Intelligence},
  address   = {Menlo Park},
  issn      = {0738-4602},
  pages     = {33 -- 44},
  year      = {2016},
  abstract  = {Answer set programming (ASP) has emerged as an approach to declarative problem solving based on the stable model semantics for logic programs. The basic idea is to represent a computational problem by a logic program, formulating constraints in terms of rules, such that its answer sets correspond to problem solutions. To this end, ASP combines an expressive language for high-level modeling with powerful low-level reasoning capacities, provided by off-the-shelf tools. Compact problem representations take advantage of genuine modeling features of ASP, including (first-order) variables, negation by default, and recursion. In this article, we demonstrate the ASP methodology on two example scenarios, illustrating basic as well as advanced modeling and solving concepts. We also discuss mechanisms to represent and implement extended kinds of preferences and optimization. An overview of further available extensions concludes the article.},
  language  = {en}
}
@article{BomansonJanhunenSchaubetal.2016,
  author    = {Bomanson, Jori and Janhunen, Tomi and Schaub, Torsten H. and Gebser, Martin and Kaufmann, Benjamin},
  title     = {Answer Set Programming Modulo Acyclicity},
  series = {Fundamenta informaticae},
  volume    = {147},
  journal   = {Fundamenta informaticae},
  publisher = {IOS Press},
  address   = {Amsterdam},
  issn      = {0169-2968},
  doi       = {10.3233/FI-2016-1398},
  pages     = {63 -- 91},
  year      = {2016},
  abstract  = {Acyclicity constraints are prevalent in knowledge representation and applications where acyclic data structures such as DAGs and trees play a role. Recently, such constraints have been considered in the satisfiability modulo theories (SMT) framework, and in this paper we carry out an analogous extension to the answer set programming (ASP) paradigm. The resulting formalism, ASP modulo acyclicity, offers a rich set of primitives to express constraints related to recursive structures. In the technical results of the paper, we relate the new generalization with standard ASP by showing (i) how acyclicity extensions translate into normal rules, (ii) how weight constraint programs can be instrumented by acyclicity extensions to capture stability in analogy to unfounded set checking, and (iii) how the gap between supported and stable models is effectively closed in the presence of such an extension. Moreover, we present an efficient implementation of acyclicity constraints by incorporating a respective propagator into the state-of-the-art ASP solver CLASP. The implementation provides a unique combination of traditional unfounded set checking with acyclicity propagation. In the experimental part, we evaluate the interplay of these orthogonal checks by equipping logic programs with supplementary acyclicity constraints. The performance results show that native support for acyclicity constraints is a worthwhile addition, furnishing a complementary modeling construct in ASP itself as well as effective means for translation-based ASP solving.},
  language  = {en}
}
@article{AbseherMusliuWoltranetal.2016,
  author    = {Abseher, Michael and Musliu, Nysret and Woltran, Stefan and Gebser, Martin and Schaub, Torsten H.},
  title     = {Shift Design with Answer Set Programming},
  series = {Fundamenta informaticae},
  volume    = {147},
  journal   = {Fundamenta informaticae},
  publisher = {IOS Press},
  address   = {Amsterdam},
  issn      = {0169-2968},
  doi       = {10.3233/FI-2016-1396},
  pages     = {1 -- 25},
  year      = {2016},
  abstract  = {Answer Set Programming (ASP) is a powerful declarative programming paradigm that has been successfully applied to many different domains. Recently, ASP has also proved successful for hard optimization problems like course timetabling and travel allotment. In this paper, we approach another important task, namely, the shift design problem, aiming at an alignment of a minimum number of shifts in order to meet required numbers of employees (which typically vary for different time periods) in such a way that over- and understaffing is minimized. We provide an ASP encoding of the shift design problem, which, to the best of our knowledge, has not been addressed by ASP yet. Our experimental results demonstrate that ASP is capable of improving the best known solutions to some benchmark problems. Other instances remain challenging and make the shift design problem an interesting benchmark for ASP-based optimization methods.},
  language  = {en}
}