AAAI 2025technical0 citations
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.
BibTeX
@article{Langley_2025, title={Learning Hierarchical Task Knowledge for Planning}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35091}, DOI={10.1609/aaai.v39i27.35091}, abstractNote={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.}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Langley, Pat}, year={2025}, month={Apr.}, pages={28652-28656} }