Redundant Decompositions in PO HTN Domains: Goto Considered Harmful. Godet, R., Bit-Monnot, A., & Lesire, C. In Proceedings of the 7th ICAPS Workshop on Hierarchical Planning (HPlan 2024), pages 36–44, 2024. ![Redundant Decompositions in PO HTN Domains: Goto Considered Harmful [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex 4 downloads HTN planning is a widely used approach for solving planning problems by breaking them down into smaller sub-problems. This approach is often motivated by the ability to add constraints between tasks, which can guide the search towards a solution and improve performance by reducing the search space. In this paper, we identify a common pattern in PO HTN planning that can lead to a pathological explosion of the search space, resulting in a significant decrease in computational performance. However, this is not a fatal issue. Alternative HTN models can be used to reduce the search space. We propose two models that maintain the expressiveness of the original problem while reducing the number of possible decompositions. Our results demonstrate improved computational performance on IPC benchmarks.
@InProceedings{Godet2024RedundantDecompositions,
author = {Roland Godet and Arthur Bit-Monnot and Charles Lesire},
booktitle = {Proceedings of the 7th ICAPS Workshop on Hierarchical Planning (HPlan 2024)},
title = {Redundant Decompositions in PO HTN Domains: Goto Considered Harmful},
year = {2024},
abstract = {HTN planning is a widely used approach for solving planning problems by breaking them down into smaller sub-problems. This approach is often motivated by the ability to add constraints between tasks, which can guide the search towards a solution and improve performance by reducing the search space. In this paper, we identify a common pattern in PO HTN planning that can lead to a pathological explosion of the search space, resulting in a significant decrease in computational performance. However, this is not a fatal issue. Alternative HTN models can be used to reduce the search space. We propose two models that maintain the expressiveness of the original problem while reducing the number of possible decompositions. Our results demonstrate improved computational performance on IPC benchmarks.},
url_paper = {https://icaps24.icaps-conference.org/program/workshops/hplan/HPlan2024_paper_9.pdf},
pages = {36--44}
}
% submission 10
Downloads: 4
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