Symmetry breaking in star-topology decoupled search

Daniel Gnad, Álvaro Torralba, Alexander Shleyfman, Jörg Hoffmann

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

12 Citations (Scopus)

Abstract

Symmetry breaking is a well-known method for search reduction. It identifies state-space symmetries prior to search, and prunes symmetric states during search. A recent proposal, star-topology decoupled search, is to search not in the state space, but in a factored version thereof, which avoids the multiplication of states across leaf components in an underlying star-topology structure. We show that, despite the much more complex structure of search states - so-called decoupled states - symmetry breaking can be brought to bear in this framework as well. Starting from the notion of structural symmetries over states, we identify a sub-class of such symmetries suitable for star-topology decoupled search, and we show how symmetries from that sub-class induce symmetry relations over decoupled states. We accordingly extend the routines required for search pruning and solution reconstruction. The resulting combined method can be exponentially better than both its components in theory, and this synergetic advantage is also manifested in practice: empirically, our method reliably inherits the best of its base components, and often outperforms them both.

Original languageEnglish
Title of host publicationProceedings of the 27th International Conference on Automated Planning and Scheduling, ICAPS 2017
EditorsLaura Barbulescu, Stephen F. Smith, Mausam, Jeremy D. Frank
Number of pages10
PublisherAAAI Press
Publication date2017
Pages125-134
ISBN (Electronic)9781577357896
Publication statusPublished - 2017
Externally publishedYes
Event27th International Conference on Automated Planning and Scheduling, ICAPS 2017 - Pittsburgh, United States
Duration: 18 Jun 201723 Jun 2017

Conference

Conference27th International Conference on Automated Planning and Scheduling, ICAPS 2017
Country/TerritoryUnited States
CityPittsburgh
Period18/06/201723/06/2017
SponsorAdventium Labs, AI Journal, Air Force Office of Scientific Research (AFOSR), David E. Smith, et al., The National Science Foundation (NSF)
SeriesProceedings International Conference on Automated Planning and Scheduling, ICAPS
ISSN2334-0835

Bibliographical note

Publisher Copyright:
Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). AH rights reserved.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

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