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
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier. This is an author produced version of a paper subsequently published in Artificial Intelligence. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Planning; Backdoors; Causal graph; Fixed-parameter tractable algorithms; (Parameterized) complexity |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Mar 2019 11:19 |
Last Modified: | 21 Dec 2019 01:39 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | https://doi.org/10.1016/j.artint.2018.10.002 |