TY  - JOUR
A1  - Schaub, Torsten H.
A1  - Woltran, Stefan
T1  - Answer set programming unleashed!
JF  - Künstliche Intelligenz
N2  - 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)]
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0550-z
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 105
EP  - 108
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Gebser, Martin
A1  - Obermeier, Philipp
A1  - Schaub, Torsten H.
A1  - Ratsch-Heitmann, Michel
A1  - Runge, Mario
T1  - Routing driverless transport vehicles in car assembly with answer set programming
JF  - Theory and practice of logic programming
N2  - Automated storage and retrieval systems are principal components of modern production and warehouse facilities. In particular, automated guided vehicles nowadays substitute human-operated pallet trucks in transporting production materials between storage locations and assembly stations. While low-level control systems take care of navigating such driverless vehicles along programmed routes and avoid collisions even under unforeseen circumstances, in the common case of multiple vehicles sharing the same operation area, the problem remains how to set up routes such that a collection of transport tasks is accomplished most effectively. We address this prevalent problem in the context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale producer of commercial vehicles, where routes for automated guided vehicles used in the production process have traditionally been hand-coded by human engineers. Such adhoc methods may suffice as long as a running production process remains in place, while any change in the factory layout or production targets necessitates tedious manual reconfiguration, not to mention the missing portability between different production plants. Unlike this, we propose a declarative approach based on Answer Set Programming to optimize the routes taken by automated guided vehicles for accomplishing transport tasks. The advantages include a transparent and executable problem formalization, provable optimality of routes relative to objective criteria, as well as elaboration tolerance towards particular factory layouts and production targets. Moreover, we demonstrate that our approach is efficient enough to deal with the transport tasks evolving in realistic production processes at the car factory of Mercedes-Benz Ludwigsfelde GmbH.
KW  - automated guided vehicle routing
KW  - car assembly operations
KW  - answer set programming
Y1  - 2018
U6  - https://doi.org/10.1017/S1471068418000182
SN  - 1471-0684
SN  - 1475-3081
VL  - 18
IS  - 3-4
SP  - 520
EP  - 534
PB  - Cambridge Univ. Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Gebser, Martin
A1  - Kaminski, Roland
A1  - Kaufmann, Benjamin
A1  - Lühne, Patrick
A1  - Obermeier, Philipp
A1  - Ostrowski, Max
A1  - Romero Davila, Javier
A1  - Schaub, Torsten H.
A1  - Schellhorn, Sebastian
A1  - Wanko, Philipp
T1  - The Potsdam Answer Set Solving Collection 5.0
JF  - Künstliche Intelligenz
N2  - 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.
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0528-x
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 181
EP  - 182
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Haubelt, Christian
A1  - Neubauer, Kai
A1  - Schaub, Torsten H.
A1  - Wanko, Philipp
T1  - Design space exploration with answer set programming
JF  - Künstliche Intelligenz
N2  - 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.
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0530-3
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 205
EP  - 206
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Banbara, Mutsunori
A1  - Inoue, Katsumi
A1  - Kaufmann, Benjamin
A1  - Okimoto, Tenda
A1  - Schaub, Torsten H.
A1  - Soh, Takehide
A1  - Tamura, Naoyuki
A1  - Wanko, Philipp
T1  - teaspoon
BT  - solving the curriculum-based course timetabling problems with answer set programming
JF  - Annals of operation research
N2  - 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.
KW  - Educational timetabling
KW  - Course timetabling
KW  - Answer set programming
KW  - Multi-objective optimization
KW  - Minimal perturbation problems
Y1  - 2018
U6  - https://doi.org/10.1007/s10479-018-2757-7
SN  - 0254-5330
SN  - 1572-9338
VL  - 275
IS  - 1
SP  - 3
EP  - 37
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Brewka, Gerhard
A1  - Ellmauthaler, Stefan
A1  - Kern-Isberner, Gabriele
A1  - Obermeier, Philipp
A1  - Ostrowski, Max
A1  - Romero, Javier
A1  - Schaub, Torsten H.
A1  - Schieweck, Steffen
T1  - Advanced solving technology for dynamic and reactive applications
JF  - Künstliche Intelligenz
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0538-8
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 199
EP  - 200
PB  - Springer
CY  - Heidelberg
ER  -