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KI - Künstliche Intelligenz
Published in: KI - Künstliche Intelligenz 2-3/2018

14-05-2018 | Systems Description

The Potsdam Answer Set Solving Collection 5.0

Authors: Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Patrick Lühne, Philipp Obermeier, Max Ostrowski, Javier Romero, Torsten Schaub, Sebastian Schellhorn, Philipp Wanko

Published in: KI - Künstliche Intelligenz | Issue 2-3/2018

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Abstract

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.
previous article The ASP System DLV: Advancements and Applications
next article Cross-Translating Answer Set Programs Using the ASPTOOLS Collection

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Literature
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Banbara M, Inoue K, Kaufmann B, Okimoto T, Schaub T, Soh T, Tamura N, Wanko P (2018) teaspoon: solving the curriculum-based course timetabling problems with answer set programming. Ann Oper Res (To appear)
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Dimopoulos Y, Gebser M, Lühne P, Romero J, Schaub T (2017) plasp 3: towards effective ASP planning. In: Balduccini M, Janhunen T (eds) Proceedings of the fourteenth international conference on logic programming and nonmonotonic reasoning (LPNMR’17). Lecture Notes in Artificial Intelligence, vol. 10377, Springer, pp 286–300
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Gebser M, Kaminski R, Kaufmann B, Ostrowski M, Schaub T, Schneider M (2011) Potassco: the potsdam answer set solving collection. AI Commun 24(2):107–124MathSciNetMATH
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Gebser M, Kaminski R, Kaufmann B, Schaub T (2012) Answer set solving in practice. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan and Claypool Publishers, San RafaelMATH
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Gebser M, Kaminski R, Schaub T (2011) aspcud: a Linux package configuration tool based on answer set programming. In: Drescher C, Lynce I, Treinen R (eds) Proceedings of the second international workshop on logics for component configuration (LoCoCo’11), electronic proceedings in theoretical computer science (EPTCS), vol 65, pp 12–25
9.
Janhunen T, Kaminski R, Ostrowski M, Schaub T, Schellhorn S, Wanko P (2017) Clingo goes linear constraints over reals and integers. Theory Prac Logic Program 17(5–6):872–888MathSciNetCrossRefMATH
10.
Kaminski R, Schaub T, Wanko P (2017) A tutorial on hybrid answer set solving with clingo. In: Ianni G, Lembo D, Bertossi L, Faber W, Glimm B, Gottlob G, Staab S (eds) Proceedings of the thirteenth international summer school of the reasoning web. Lecture Notes in Computer Science, vol 10370, Springer, pp 167–203
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Kaufmann B, Leone N, Perri S, Schaub T (2016) Grounding and solving in answer set programming. AI Mag 37(3):25–32CrossRef
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Newman N, Fréchette A, Leyton-Brown K (2018) Deep optimization for spectrum repacking. Commun ACM 61(1):97–104CrossRef
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Metadata
Title
The Potsdam Answer Set Solving Collection 5.0
Authors
Martin Gebser
Roland Kaminski
Benjamin Kaufmann
Patrick Lühne
Philipp Obermeier
Max Ostrowski
Javier Romero
Torsten Schaub
Sebastian Schellhorn
Philipp Wanko
Publication date
14-05-2018
Publisher
Springer Berlin Heidelberg
Published in
KI - Künstliche Intelligenz / Issue 2-3/2018
Print ISSN: 0933-1875
Electronic ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-018-0528-x

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