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Interval Based Relaxation Heuristics for Numeric Planning with Action Costs

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KI 2017: Advances in Artificial Intelligence (KI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10505))

Abstract

Many real-world problems can be expressed in terms of states and actions that modify the world to reach a certain goal. Such problems can be solved by automated planning. Numeric planning supports numeric quantities such as resources or physical properties in addition to the propositional variables from classical planning. We approach numeric planning with heuristic search and introduce adaptations of the relaxation heuristics \(h_\text {max}\), \(h_\text {add}\) and \(h_{\text {FF}}\) to interval based relaxation frameworks. In contrast to previous approaches, the heuristics presented in this paper are not limited to fragments of numeric planning with instantaneous actions (such as linear or acyclic numeric planning tasks) and support action costs.

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Notes

  1. 1.

    Scala et al. [17] run into a similar problem using “asynchronous subgoaling” and have to set the cost of hard conditions to 0 to ensure admissibility of \(h_\text {max}\).

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Acknowledgments

This work was supported by the DFG through grants EXC1086 (BrainLinks-BrainTools) and NE 623/13-2 (HYBRIS-2).

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Authors and Affiliations

  1. Department of Computer Science, University of Freiburg, Freiburg, Germany

    Johannes Aldinger & Bernhard Nebel

Authors
  1. Johannes Aldinger
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  2. Bernhard Nebel
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Correspondence to Johannes Aldinger .

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Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany

    Gabriele Kern-Isberner

  2. FB Informatik, TU Darmstadt, Darmstadt, Hessen, Germany

    Johannes Fürnkranz

  3. FB Informatik, Universität Koblenz, Koblenz, Rheinland-Pfalz, Germany

    Matthias Thimm

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Aldinger, J., Nebel, B. (2017). Interval Based Relaxation Heuristics for Numeric Planning with Action Costs. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-67190-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67189-5

  • Online ISBN: 978-3-319-67190-1

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