Learning Hierarchical Task Knowledge for Planning
Abstract
In this paper, I review approaches for acquiring hierarchical knowledge to improve the effectiveness of planning systems. First I note some benefits of such hierarchical content and the advantages of learning over manual construction. After this, I consider alternative paradigms for encoding and acquiring plan expertise before turning to hierarchical task networks. I specify the inputs to HTN learners and three subproblems they must address: identifying hierarchical structure, unifying method heads, and finding method conditions. Finally, I pose seven challenges the community should pursue so that techniques for learning HTNs can reach their full potential.
Cite
Text
Langley. "Learning Hierarchical Task Knowledge for Planning." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I27.35091Markdown
[Langley. "Learning Hierarchical Task Knowledge for Planning." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/langley2025aaai-learning/) doi:10.1609/AAAI.V39I27.35091BibTeX
@inproceedings{langley2025aaai-learning,
title = {{Learning Hierarchical Task Knowledge for Planning}},
author = {Langley, Pat},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {28652-28656},
doi = {10.1609/AAAI.V39I27.35091},
url = {https://mlanthology.org/aaai/2025/langley2025aaai-learning/}
}