Complexity bounds for the controllability of temporal networks with conditions, disjunctions, and uncertainty
Name
1901.02307.pdf
Description
Accepted version
Size
858.86 KB
Format
Adobe PDF
Checksum (MD5)
04c5fe481b0d14ac9074718a6ae35802
Author(s)
Bhargava, Nikhil
Williams, Brian C
Date Issued
January 2019
Journal
Artificial Intelligence
Publisher
Elsevier BV
Citation
Bhargava, Nikhil, and Brian C. Williams. "Complexity bounds for the controllability of temporal networks with conditions, disjunctions, and uncertainty." Artificial Intelligence, 271 (June 2019): 1-17.
Version
Author's final manuscript
Abstract
In temporal planning, many different temporal network formalisms are used to model real world situations. Each of these formalisms has different features which affect how easy it is to determine whether the underlying network of temporal constraints is consistent. While many of the simpler models have been well-studied from a computational complexity perspective, the algorithms developed for advanced models which combine features have very loose complexity bounds. In this paper, we provide tight completeness bounds for strong, weak, and dynamic controllability checking of temporal networks that have conditions, disjunctions, and temporal uncertainty. Our work exposes some of the subtle differences between these different structures and, remarkably, establishes a guarantee that all of these problems are computable in PSPACE.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.artint.2018.11.008
