Symbolic Search for Optimal Total-Order HTN Planning
Abstract
Symbolic search has proven to be a useful approach to optimal classical planning. In Hierarchical Task Network (HTN) planning, however, there is little work on optimal planning. One reason for this is that in HTN planning, most algorithms are based on heuristic search, and admissible heuristics have to incorporate the structure of the task network in order to be informative. In this paper, we present a novel approach to optimal (totally-ordered) HTN planning, which is based on symbolic search. An empirical analysis shows that our symbolic approach outperforms the current state of the art for optimal totally-ordered HTN planning.
Cite
Text
Behnke and Speck. "Symbolic Search for Optimal Total-Order HTN Planning." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I13.17396Markdown
[Behnke and Speck. "Symbolic Search for Optimal Total-Order HTN Planning." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/behnke2021aaai-symbolic/) doi:10.1609/AAAI.V35I13.17396BibTeX
@inproceedings{behnke2021aaai-symbolic,
title = {{Symbolic Search for Optimal Total-Order HTN Planning}},
author = {Behnke, Gregor and Speck, David},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {11744-11754},
doi = {10.1609/AAAI.V35I13.17396},
url = {https://mlanthology.org/aaai/2021/behnke2021aaai-symbolic/}
}