IJCAI 2020poster0 citations
Lagrangian Decomposition for Classical Planning (Extended Abstract)
Florian Pommerening, Gabriele Röger, Malte Helmert, Hadrien Cambazad, Louis-Martin Rousseau, Domenico Salvagnin
Abstract
Optimal cost partitioning of classical planning heuristics has been shown to lead to excellent heuristic values but is often prohibitively expensive to compute. We analyze the application of Lagrangian decomposition, a classical tool in mathematical programming, to cost partitioning of operator-counting heuristics. This allows us to view the computation as an iterative process that can be seeded with any cost partitioning and that improves over time. In the case of non-negative cost partitioning of abstraction heuristics the computation reduces to independent shortest path problems and does not require an LP solver.
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BibTeX
@inproceedings{ijcai2020p663,
title = {Lagrangian Decomposition for Classical Planning (Extended Abstract)},
author = {Pommerening, Florian and Röger, Gabriele and Helmert, Malte and Cambazad, Hadrien and Rousseau, Louis-Martin and Salvagnin, Domenico},
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 = {4770--4774},
year = {2020},
month = {7},
note = {Sister Conferences Best Papers},
doi = {10.24963/ijcai.2020/663},
url = {https://doi.org/10.24963/ijcai.2020/663},
}