Optimizing the Optimization of Planning Domains by Automatic Action Schema Splitting
Abstract
Most planners are based on grounding, that is, generating all instances of a parameterized action during a preprocessing phase. For some problems the number of ground actions is too high, causing a performance bottleneck. Building upon an existing approach, we present an enhanced method to split action schemas automatically during the grounding phase, to reduce the number of ground actions. First, we propose to exploit the structural knowledge of the problems to have a more informative dependency graph. Then, we suggest a better objective function to define and choose the best split. Finally, we present a more effective search to find it. We experimentally measure the impact of each of these improvements, and show that our approach significantly outperforms the state of the art.
Cite
Text
Elahi and Rintanen. "Optimizing the Optimization of Planning Domains by Automatic Action Schema Splitting." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I18.29988Markdown
[Elahi and Rintanen. "Optimizing the Optimization of Planning Domains by Automatic Action Schema Splitting." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/elahi2024aaai-optimizing/) doi:10.1609/AAAI.V38I18.29988BibTeX
@inproceedings{elahi2024aaai-optimizing,
title = {{Optimizing the Optimization of Planning Domains by Automatic Action Schema Splitting}},
author = {Elahi, Mojtaba and Rintanen, Jussi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {20096-20103},
doi = {10.1609/AAAI.V38I18.29988},
url = {https://mlanthology.org/aaai/2024/elahi2024aaai-optimizing/}
}