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KI - Künstliche Intelligenz

, Volume 32, Issue 2–3, pp 199–200 | Cite as

Advanced Solving Technology for Dynamic and Reactive Applications

  • Gerhard Brewka
  • Stefan Ellmauthaler
  • Gabriele Kern-Isberner
  • Philipp Obermeier
  • Max Ostrowski
  • Javier Romero
  • Torsten Schaub
  • Steffen Schieweck
Project Report
  • 53 Downloads

Aims of the Project

The project Advanced Solving Technology for Dynamic and Reactive Applications (henceforth called ASTRA) is part of the DFG-funded Research Unit HYBRIS: Hybrid Reasoning for Intelligent Systems (www.hybrid-reasoning.org/). The Unit started in 2012 with the aim of investigating different combinations of both qualitative and quantitative reasoning. Among the quantitative aspects addressed are time, uncertainty, preferences, continuous state spaces, and quantitative data such as point clouds or text, from which meaningful symbolic descriptions can be extracted.

The principal investigators of ASTRA are Gerhard Brewka (Leipzig), Gabriele Kern-Isberner (Dortmund) and Torsten Schaub (Potsdam). In a nutshell, the project aims to provide hybrid reasoning methods that are sufficiently expressive to handle complex decision-making problems. So far our research focused on answer set solving technology for incremental and reactive reasoning, preferential reasoning, and finite...

References

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    Banbara M, Kaufmann B, Ostrowski M, Schaub T (2017) Clingcon: the next generation. Theory Pract Logic Progr 17(4):408–461MathSciNetCrossRefzbMATHGoogle Scholar
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    Janhunen T, Kaminski R, Ostrowski M, Schaub T, Schellhorn S, Wanko P (2017) Clingo goes linear constraints over reals and integers. Theory Pract Logic Progr 17(5–6):872–888MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Schieweck S, Kern-Isberner G, ten Hompel M (2016) Using answer set programming in an order-picking system with cellular transport vehicles. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp 1600–1604. IEEEGoogle Scholar
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    Schieweck S, Kern-Isberner G, ten Hompel M (2017) Various approaches to the application of answer set programming in order-picking systems with intelligent vehicles. In: International Joint Conference on Computational Intelligence, pp 25–34Google Scholar
  5. 5.
    Schieweck S, Kern-Isberner G, ten Hompel M (2017) Planung von Intralogistiksystemen mit Hilfe von Antwortmengenprogrammierung. In: Logistics Journal: Proceedings. Wissenschaftliche Gesellschaft für Technische LogistikGoogle Scholar
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    Nguyen V, Obermeier P, Son T, Schaub T, Yeoh W (2017) Generalized target assignment and path finding using answer set programming. In: Proc. IJCAI-17, pp 1216–1223Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of LeipzigLeipzigGermany
  2. 2.University of DortmundDortmundGermany
  3. 3.University of PotsdamPotsdamGermany

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