IJCAI 2020poster0 citations
Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning
Silvan Sievers, Florian Pommerening, Thomas Keller, Malte Helmert
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.
Planning and Scheduling: Planning AlgorithmsPlanning and Scheduling: Search in Planning and SchedulingHeuristic Search and Game Playing: Heuristic Search
BibTeX
@inproceedings{ijcai2020p574,
title = {Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning},
author = {Sievers, Silvan and Pommerening, Florian and Keller, Thomas and Helmert, Malte},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {4152--4160},
year = {2020},
month = {7},
note = {Main track},
doi = {10.24963/ijcai.2020/574},
url = {https://doi.org/10.24963/ijcai.2020/574},
}