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HTN Plan Repair via Model Transformation

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KI 2020: Advances in Artificial Intelligence (KI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12325))

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Abstract

To make planning feasible, planning models abstract from many details of the modeled system. When executing plans in the actual system, the model might be inaccurate in a critical point, and plan execution may fail. There are two options to handle this case: the previous solution can be modified to address the failure (plan repair), or the planning process can be re-started from the new situation (re-planning). In HTN planning, discarding the plan and generating a new one from the novel situation is not easily possible, because the HTN solution criteria make it necessary to take already executed actions into account. Therefore all approaches to repair plans in the literature are based on specialized algorithms. In this paper, we discuss the problem in detail and introduce a novel approach that makes it possible to use unchanged, off-the-shelf HTN planning systems to repair broken HTN plans. That way, no specialized solvers are needed.

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Notes

  1. 1.

    To simplify the following definitions, the definition is slightly different from Definition 1, where it is a sequence of identifiers mapped to the tasks. The latter makes it possible to identify which decomposition resulted in an action, which is not needed here.

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Acknowledgments

Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 232722074 – SFB 1102 / Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 232722074 – SFB 1102.

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Authors and Affiliations

  1. Saarland University, Saarland Informatics Campus, Saarbrücken, Germany

    Daniel Höller

  2. The Australian National University, Canberra, Australia

    Pascal Bercher

  3. University of Freiburg, Freiburg, Germany

    Gregor Behnke

  4. Institute of Artificial Intelligence, Ulm University, Ulm, Germany

    Daniel Höller, Pascal Bercher, Gregor Behnke & Susanne Biundo

Authors
  1. Daniel Höller
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  2. Pascal Bercher
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  3. Gregor Behnke
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  4. Susanne Biundo
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Corresponding author

Correspondence to Daniel Höller .

Editor information

Editors and Affiliations

  1. Universität Bamberg, Bamberg, Germany

    Ute Schmid

  2. Örebro University, Örebro, Sweden

    Franziska Klügl

  3. Universität Bamberg, Bamberg, Germany

    Diedrich Wolter

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Höller, D., Bercher, P., Behnke, G., Biundo, S. (2020). HTN Plan Repair via Model Transformation. In: Schmid, U., Klügl, F., Wolter, D. (eds) KI 2020: Advances in Artificial Intelligence. KI 2020. Lecture Notes in Computer Science(), vol 12325. Springer, Cham. https://doi.org/10.1007/978-3-030-58285-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-58285-2_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-58285-2

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