Merge-and-Shrink Task Reformulation for Classical Planning
Abstract
The performance of domain-independent planning systems heavily depends on how the planning task has been modeled. This makes task reformulation an important tool to get rid of unnecessary complexity and increase the robustness of planners with respect to the model chosen by the user. In this paper, we represent tasks as factored transition systems (FTS), and use the merge-and-shrink (M&S) framework for task reformulation for optimal and satisficing planning. We prove that the flexibility of the underlying representation makes the M&S reformulation methods more powerful than the counterparts based on the more popular finite-domain representation. We adapt delete-relaxation and M&S heuristics to work on the FTS representation and evaluate the impact of our reformulation.
Cite
Text
Torralba and Sievers. "Merge-and-Shrink Task Reformulation for Classical Planning." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/783Markdown
[Torralba and Sievers. "Merge-and-Shrink Task Reformulation for Classical Planning." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/torralba2019ijcai-merge/) doi:10.24963/IJCAI.2019/783BibTeX
@inproceedings{torralba2019ijcai-merge,
title = {{Merge-and-Shrink Task Reformulation for Classical Planning}},
author = {Torralba, Álvaro and Sievers, Silvan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {5644-5652},
doi = {10.24963/IJCAI.2019/783},
url = {https://mlanthology.org/ijcai/2019/torralba2019ijcai-merge/}
}