@article{BesnardSchaub1995, author = {Besnard, Philippe and Schaub, Torsten}, title = {An approach to context-based default reasoning}, issn = {0169-2968}, year = {1995}, language = {en} } @article{DelgrandeLiuSchaubetal.2006, author = {Delgrande, James Patrick and Liu, Daphne H. and Schaub, Torsten and Thiele, Sven}, title = {COBA 2.0 : a consistency-based belief change system}, year = {2006}, language = {en} } @article{NicolasSchaub1998, author = {Nicolas, Pascal and Schaub, Torsten}, title = {Un cadre g{\´e}n{\´e}ral pour l'interrogation automatique en logiques des d{\´e}fauts}, year = {1998}, language = {fr} } @article{LindauerHoosHutteretal.2015, author = {Lindauer, Marius and Hoos, Holger H. and Hutter, Frank and Schaub, Torsten}, title = {An automatically configured algorithm selector}, series = {The journal of artificial intelligence research}, volume = {53}, journal = {The journal of artificial intelligence research}, publisher = {AI Access Foundation}, address = {Marina del Rey}, issn = {1076-9757}, pages = {745 -- 778}, year = {2015}, abstract = {Algorithm selection (AS) techniques - which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently - have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures, and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4.}, language = {en} } @article{HoosLindauerSchaub2014, author = {Hoos, Holger and Lindauer, Marius and Schaub, Torsten}, title = {claspfolio 2}, series = {Theory and practice of logic programming}, volume = {14}, journal = {Theory and practice of logic programming}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068414000210}, pages = {569 -- 585}, year = {2014}, abstract = {Building on the award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2, a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques. The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques. The default configuration of claspfolio 2 relies on a light-weight version of the ASP solver clasp to generate static and dynamic instance features. The flexible open design of claspfolio 2 is a distinguishing factor even beyond ASP. As such, it provides a unique framework for comparing and combining existing portfolio-based algorithm selection approaches and techniques in a single, unified framework. Taking advantage of this, we conducted an extensive experimental study to assess the impact of different feature sets, selection approaches and base solver portfolios. In addition to gaining substantial insights into the utility of the various approaches and techniques, we identified a default configuration of claspfolio 2 that achieves substantial performance gains not only over clasp's default configuration and the earlier version of claspfolio, but also over manually tuned configurations of clasp.}, language = {en} } @article{TranPontelliBalduccinietal.2022, author = {Tran, Son Cao and Pontelli, Enrico and Balduccini, Marcello and Schaub, Torsten}, title = {Answer set planning}, series = {Theory and practice of logic programming}, journal = {Theory and practice of logic programming}, publisher = {Cambridge University Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068422000072}, pages = {73}, year = {2022}, abstract = {Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, that is, solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.}, language = {en} } @article{BrainGebserPuehreretal.2007, author = {Brain, Martin and Gebser, Martin and P{\"u}hrer, J{\"o}rg and Schaub, Torsten and Tompits, Hans and Woltran, Stefan}, title = {Debugging ASP programs by means of ASP}, isbn = {978-3-540- 72199-4}, year = {2007}, language = {en} } @article{BanbaraInoueKaufmannetal.2018, author = {Banbara, Mutsunori and Inoue, Katsumi and Kaufmann, Benjamin and Okimoto, Tenda and Schaub, Torsten and Soh, Takehide and Tamura, Naoyuki and Wanko, Philipp}, title = {teaspoon}, series = {Annals of operation research}, volume = {275}, journal = {Annals of operation research}, number = {1}, publisher = {Springer}, address = {Dordrecht}, issn = {0254-5330}, doi = {10.1007/s10479-018-2757-7}, pages = {3 -- 37}, year = {2018}, abstract = {Answer Set Programming (ASP) is an approach to declarative problem solving, combining a rich yet simple modeling language with high performance solving capacities. We here develop an ASP-based approach to curriculum-based course timetabling (CB-CTT), one of the most widely studied course timetabling problems. The resulting teaspoon system reads a CB-CTT instance of a standard input format and converts it into a set of ASP facts. In turn, these facts are combined with a first-order encoding for CB-CTT solving, which can subsequently be solved by any off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting it to the best known bounds obtained so far via dedicated implementations. Furthermore, we extend the teaspoon system to multi-objective course timetabling and consider minimal perturbation problems.}, language = {en} } @article{SchaubWoltran2018, author = {Schaub, Torsten 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} } @article{HaubeltNeubauerSchaubetal.2018, author = {Haubelt, Christian and Neubauer, Kai and Schaub, Torsten and Wanko, Philipp}, title = {Design space exploration with 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/s13218-018-0530-3}, pages = {205 -- 206}, year = {2018}, abstract = {The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.}, language = {en} }