Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning
Abstract
Cost partitioning is a method for admissibly combining admissible heuristics. In this work, we extend this concept to merge-and-shrink (M&S) abstractions that may use labels that do not directly correspond to operators. We investigate how optimal and saturated cost partitioning (SCP) interact with M&S transformations and develop a method to compute SCPs during the computation of M&S. Experiments show that SCP significantly improves M&S on standard planning benchmarks.
Cite
Text
Sievers et al. "Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/574Markdown
[Sievers et al. "Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/sievers2020ijcai-cost/) doi:10.24963/IJCAI.2020/574BibTeX
@inproceedings{sievers2020ijcai-cost,
title = {{Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning}},
author = {Sievers, Silvan and Pommerening, Florian and Keller, Thomas and Helmert, Malte},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {4152-4160},
doi = {10.24963/IJCAI.2020/574},
url = {https://mlanthology.org/ijcai/2020/sievers2020ijcai-cost/}
}