@article{DelgrandeSchaubTompitsetal.2004,
  author    = {Delgrande, James Patrick and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan},
  title     = {On Computing belief change operations using quantifield boolean formulas},
  issn      = {0955-792X},
  year      = {2004},
  abstract  = {In this paper, we show how an approach to belief revision and belief contraction can be axiomatized by means of quantified Boolean formulas. Specifically, we consider the approach of belief change scenarios, a general framework that has been introduced for expressing different forms of belief change. The essential idea is that for a belief change scenario (K, R, C), the set of formulas K, representing the knowledge base, is modified so that the sets of formulas R and C are respectively true in, and consistent with the result. By restricting the form of a belief change scenario, one obtains specific belief change operators including belief revision, contraction, update, and merging. For both the general approach and for specific operators, we give a quantified Boolean formula such that satisfying truth assignments to the free variables correspond to belief change extensions in the original approach. Hence, we reduce the problem of determining the results of a belief change operation to that of satisfiability. This approach has several benefits. First, it furnishes an axiomatic specification of belief change with respect to belief change scenarios. This then leads to further insight into the belief change framework. Second, this axiomatization allows us to identify strict complexity bounds for the considered reasoning tasks. Third, we have implemented these different forms of belief change by means of existing solvers for quantified Boolean formulas. As well, it appears that this approach may be straightforwardly applied to other specific approaches to belief change},
  language  = {en}
}
@article{DelgrandeSchaubTompitsetal.2001,
  author    = {Delgrande, James Patrick and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan},
  title     = {On computing solutions to belief change scenarios},
  isbn      = {3-540- 42464-4},
  year      = {2001},
  language  = {en}
}
@article{PearceSarsakovSchaubetal.2002,
  author    = {Pearce, David and Sarsakov, Vladimir and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan},
  title     = {A polynomial translation of logic programs with nested expressions into disjunctive logic programs},
  isbn      = {3-540-43930-7},
  year      = {2002},
  language  = {en}
}
@article{BesnardSchaubTompitsetal.2002,
  author    = {Besnard, Philippe and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan},
  title     = {Paraconsistent reasoning via quantified boolean formulas},
  isbn      = {3-540-44190-5},
  year      = {2002},
  language  = {en}
}
@article{BrainGebserPuehreretal.2007,
  author    = {Brain, Martin and Gebser, Martin and P{\"u}hrer, J{\"o}rg and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan},
  title     = {"That is illogical, Captain!" : the debugging support tool spock for answer-set programs ; system description},
  year      = {2007},
  language  = {en}
}
@article{PearceSarsakovSchaubetal.2002,
  author    = {Pearce, David and Sarsakov, Vladimir and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan},
  title     = {A polynomial translation of logic programs with nested expressions into disjunctive logic programs : preliminary report},
  year      = {2002},
  language  = {en}
}
@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}
}
@article{BesnardSchaubTompitsetal.2003,
  author    = {Besnard, Philippe and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan},
  title     = {Paraconsistent reasoning via quantified boolean formulas : Part II: Circumscribing inconsistent theories},
  isbn      = {3-540- 409494-5},
  year      = {2003},
  language  = {en}
}
@article{DelgrandeSchaubTompitsetal.2013,
  author    = {Delgrande, James and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan},
  title     = {A model-theoretic approach to belief change in answer set programming},
  series = {ACM transactions on computational logic},
  volume    = {14},
  journal   = {ACM transactions on computational logic},
  number    = {2},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  issn      = {1529-3785},
  doi       = {10.1145/2480759.2480766},
  pages     = {46},
  year      = {2013},
  abstract  = {We address the problem of belief change in (nonmonotonic) logic programming under answer set semantics. Our formal techniques are analogous to those of distance-based belief revision in propositional logic. In particular, we build upon the model theory of logic programs furnished by SE interpretations, where an SE interpretation is a model of a logic program in the same way that a classical interpretation is a model of a propositional formula. Hence we extend techniques from the area of belief revision based on distance between models to belief change in logic programs. We first consider belief revision: for logic programs P and Q, the goal is to determine a program R that corresponds to the revision of P by Q, denoted P * Q. We investigate several operators, including (logic program) expansion and two revision operators based on the distance between the SE models of logic programs. It proves to be the case that expansion is an interesting operator in its own right, unlike in classical belief revision where it is relatively uninteresting. Expansion and revision are shown to satisfy a suite of interesting properties; in particular, our revision operators satisfy all or nearly all of the AGM postulates for revision. We next consider approaches for merging a set of logic programs, P-1,...,P-n. Again, our formal techniques are based on notions of relative distance between the SE models of the logic programs. Two approaches are examined. The first informally selects for each program P-i those models of P-i that vary the least from models of the other programs. The second approach informally selects those models of a program P-0 that are closest to the models of programs P-1,...,P-n. In this case, P-0 can be thought of as a set of database integrity constraints. We examine these operators with regards to how they satisfy relevant postulate sets. Last, we present encodings for computing the revision as well as the merging of logic programs within the same logic programming framework. This gives rise to a direct implementation of our approach in terms of off-the-shelf answer set solvers. These encodings also reflect the fact that our change operators do not increase the complexity of the base formalism.},
  language  = {en}
}