Symmetry Detection and Breaking in Linear Cost-Optimal Numeric Planning

Alexander Shleyfman, Ryo Kuroiwa, J. Christopher Beck

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

One of the main challenges of domain-independent numeric planning is the complexity of the search problem. The exploitation of structural symmetries in a search problem can constitute an effective method of pruning search branches that may lead to exponential improvements in performance. For over a decade, symmetry breaking techniques have been successfully used within both optimal and satisficing classical planning. In this work, we show that symmetry detection methods applied in classical planning, with some effort, can be modified to detect symmetries in linear numeric planning. The detected symmetry group, thereafter, can be used almost directly in the A*-based symmetry breaking algorithms such as DKS and Orbit Space Search. We empirically validate that symmetry pruning can yield a substantial reduction in the search effort, even if algorithms are equipped with a strong heuristic, such as LM-cut.

Original languageEnglish
Pages (from-to)393-401
Number of pages9
JournalProceedings International Conference on Automated Planning and Scheduling, ICAPS
Volume33
Issue number1
DOIs
StatePublished - 2023
Event33rd International Conference on Automated Planning and Scheduling, ICAPS 2023 - Prague, Czech Republic
Duration: 8 Jul 202313 Jul 2023

Bibliographical note

Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

This work is partially supported by the Natural Sciences and Engineering Research Council of Canada. Alexander Sh-leyfman is partially supported by the Israel Academy of Sciences and Humanities program for Israeli postdocts.

FundersFunder number
Israel Academy of Sciences and Humanities program for Israeli postdocts
Natural Sciences and Engineering Research Council of Canada

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