Kronegger, M, Ordyniak, S orcid.org/0000-0003-1935-651X and Pfandler, A (2019) Backdoors to planning. Artificial Intelligence, 269. pp. 49-75. ISSN 0004-3702
Abstract
Backdoors measure the distance to tractable fragments and have become an important tool to find fixed-parameter tractable (fpt) algorithms for hard problems in AI and beyond. Despite their success, backdoors have not been used for planning, a central problem in AI that has a high computational complexity. In this work, we introduce two notions of backdoors building upon the causal graph. We analyze the complexity of finding a small backdoor (detection) and using the backdoor to solve the problem (evaluation) in the light of planning with (un)bounded plan length/domain of the variables. For each setting we present either an fpt-result or rule out the existence thereof by showing parameterized intractability. For several interesting cases we achieve the most desirable outcome: detection and evaluation are fpt. In addition, we explore the power of polynomial preprocessing for all fpt-results, i.e., we investigate whether polynomial kernels exist. We show that for the detection problems, polynomial kernels exist whereas we rule out the existence of polynomial kernels for the evaluation problems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Published by Elsevier B.V. This is an author produced version of an article published in Artificial Intelligence. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Planning; Backdoors; Causal graph; Fixed-parameter tractable algorithms; (Parameterized) complexity |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Nov 2020 15:18 |
Last Modified: | 17 Nov 2020 15:18 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.artint.2018.10.002 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168076 |