Dabrowski, KK, Jonsson, P, Ordyniak, S orcid.org/0000-0003-1935-651X et al. (1 more author) (2022) Resolving Inconsistencies in Simple Temporal Problems: A Parameterized Approach. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence: AAAI-22 Technical Tracks 4. Thirty-Sixth AAAI Conference on Artificial Intelligence, 22 Feb - 01 Mar 2022, Virtual. AAAI Press , Palo Alto, California USA , pp. 3724-3732. ISBN 978-1-57735-876-3
Abstract
The simple temporal problem (STP) is one of the most influential reasoning formalisms for representing temporal information in AI. We study the problem of resolving inconsistency of data encoded in the STP. We prove that the problem of identifying a maximally large consistent subset of data is NP-hard. In practical instances, it is reasonable to assume that the amount of erroneous data is small. We therefore parameterize by the number of constraints that need to be removed to achieve consistency. Using tools from parameterized complexity we design fixed-parameter tractable algorithms for two large fragments of the STP. Our main algorithmic results employ reductions to the Directed Subset Feedback Arc Set problem and iterative compression combined with an efficient algorithm for the Edge Multicut problem. We complement our algorithmic results with hardness results that rule out fixed-parameter tractable algorithms for all remaining non-trivial fragments of the STP (under standard complexity-theoretic assumptions). Together, our results give a full classification of the classical and parameterized complexity of the problem.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Constraint Satisfaction And Optimization (CSO) |
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) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/V00252X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Feb 2022 15:26 |
Last Modified: | 17 Oct 2023 13:14 |
Status: | Published |
Publisher: | AAAI Press |
Identification Number: | 10.1609/aaai.v36i4.20286 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184057 |