Efficient Black-Box Planning Using Macro-Actions with Focused Effects
Abstract
The difficulty of deterministic planning increases exponentially with search-tree depth. Black-box planning presents an even greater challenge, since planners must operate without an explicit model of the domain. Heuristics can make search more efficient, but goal-aware heuristics for black-box planning usually rely on goal counting, which is often quite uninformative. In this work, we show how to overcome this limitation by discovering macro-actions that make the goal-count heuristic more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.
Cite
Text
Allen et al. "Efficient Black-Box Planning Using Macro-Actions with Focused Effects." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/554Markdown
[Allen et al. "Efficient Black-Box Planning Using Macro-Actions with Focused Effects." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/allen2021ijcai-efficient/) doi:10.24963/IJCAI.2021/554BibTeX
@inproceedings{allen2021ijcai-efficient,
title = {{Efficient Black-Box Planning Using Macro-Actions with Focused Effects}},
author = {Allen, Cameron and Katz, Michael and Klinger, Tim and Konidaris, George and Riemer, Matthew and Tesauro, Gerald},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4024-4031},
doi = {10.24963/IJCAI.2021/554},
url = {https://mlanthology.org/ijcai/2021/allen2021ijcai-efficient/}
}