Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning

IJCAI, pp. 4152-4160, 2020.

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factored transition systemlinear programpartial state sclassical planningcartesian abstractionMore(11+)
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Our theoretical analysis investigates the interaction of Optimal cost partitioning and saturated cost partitioning with M&S transformations, and our practical implementation significantly improves regular M&S heuristics

Abstract:

Cost partitioning is a method for admissibly combining admissible heuristics. In this work, we extend this concept to merge-and-shrink (M&S) abstractions that may use labels that do not directly correspond to operators. We investigate how optimal and saturated cost partitioning (SCP) interact with M&S transformations and devel...More

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Introduction
  • Classical planning [Ghallab et al, 2004] aims to find a sequence of actions that leads from an initial world situation to a specified goal.
  • A possible solution to this problem is the combination of different abstractions admissibly with operator cost partitioning [Katz and Domshlak, 2010; Seipp et al, 2017a], which distributes the operator costs among the abstractions guaranteeing that the sum of their heuristic values is admissible
  • It has successfully been used with Cartesian abstractions [Seipp and Helmert, 2018] and pattern databases [Edelkamp, 2001], but not yet with M&S.
Highlights
  • Classical planning [Ghallab et al, 2004] aims to find a sequence of actions that leads from an initial world situation to a specified goal
  • A possible solution to this problem is the combination of different abstractions admissibly with operator cost partitioning [Katz and Domshlak, 2010; Seipp et al, 2017a], which distributes the operator costs among the abstractions guaranteeing that the sum of their heuristic values is admissible
  • 4.3 Merging The previous two sections showed transformations that can potentially decrease the value of hOCP. In contrast to these results, we show that the heuristic value of hOCP can only increase after merging
  • Thereafter, we show that exact label reduction does not affect the value of hSCP
  • We focus on saturated cost partitioning because computing Optimal cost partitioning over large abstractions is often infeasible in practice, but our approach can be used with Optimal cost partitioning in the same way
  • Our theoretical analysis investigates the interaction of Optimal cost partitioning and saturated cost partitioning with M&S transformations, and our practical implementation significantly improves regular M&S heuristics
Methods
  • The authors implemented the techniques on top of the M&S implementation of Fast Downward [Helmert, 2006], version 19.12.
  • The authors use the tasks of all optimal tracks of all International Planning Competitions, a set consisting of 1827 tasks across 65 domains.
  • Experiments were run on Intel Xeon Silver 4114 CPUs, using Downward Lab [Seipp et al, 2017b].
  • Each planner run is limited to 1800s and 3.5 GiB.
  • The code, benchmarks, and experimental data are published online [Sievers et al, 2020a]
Results
  • The authors' theoretical analysis investigates the interaction of OCP and SCP with M&S transformations, and the practical implementation significantly improves regular M&S heuristics.
Conclusion
  • The authors contribute the first combination of cost partitioning with M&S heuristics.
  • The authors' theoretical analysis investigates the interaction of OCP and SCP with M&S transformations, and the practical implementation significantly improves regular M&S heuristics.
  • The authors want to investigate better order strategies for SCPs in the context of M&S and to formalize cost partitioning as a transformation of the M&S framework
Summary
  • Introduction:

    Classical planning [Ghallab et al, 2004] aims to find a sequence of actions that leads from an initial world situation to a specified goal.
  • A possible solution to this problem is the combination of different abstractions admissibly with operator cost partitioning [Katz and Domshlak, 2010; Seipp et al, 2017a], which distributes the operator costs among the abstractions guaranteeing that the sum of their heuristic values is admissible
  • It has successfully been used with Cartesian abstractions [Seipp and Helmert, 2018] and pattern databases [Edelkamp, 2001], but not yet with M&S.
  • Methods:

    The authors implemented the techniques on top of the M&S implementation of Fast Downward [Helmert, 2006], version 19.12.
  • The authors use the tasks of all optimal tracks of all International Planning Competitions, a set consisting of 1827 tasks across 65 domains.
  • Experiments were run on Intel Xeon Silver 4114 CPUs, using Downward Lab [Seipp et al, 2017b].
  • Each planner run is limited to 1800s and 3.5 GiB.
  • The code, benchmarks, and experimental data are published online [Sievers et al, 2020a]
  • Results:

    The authors' theoretical analysis investigates the interaction of OCP and SCP with M&S transformations, and the practical implementation significantly improves regular M&S heuristics.
  • Conclusion:

    The authors contribute the first combination of cost partitioning with M&S heuristics.
  • The authors' theoretical analysis investigates the interaction of OCP and SCP with M&S transformations, and the practical implementation significantly improves regular M&S heuristics.
  • The authors want to investigate better order strategies for SCPs in the context of M&S and to formalize cost partitioning as a transformation of the M&S framework
Tables
  • Table1: Table 1
  • Table2: Coverage with interleaved or offline computation using different order strategies
Download tables as Excel
Funding
  • We have received funding for this work from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 817639)
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