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dc.contributor.authorBhargava, Nikhil
dc.contributor.authorMuise, Christian
dc.contributor.authorWilliams, Brian
dc.date.accessioned2020-06-08T14:56:21Z
dc.date.available2020-06-08T14:56:21Z
dc.date.issued2018-07
dc.identifier.isbn978-0-9992411-2-7
dc.identifier.urihttps://hdl.handle.net/1721.1/125711
dc.description.abstractIn temporal planning, agents must schedule a set of events satisfying a set of predetermined constraints. These scheduling problems become more difficult when the duration of certain actions are outside the agent's control. Delay controllability is the generalized notion of whether a schedule can be constructed in the face of uncertainty if the agent eventually learns when events occur. Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed. We provide an efficient O(n3) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms. We conclude by providing empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems.en_US
dc.description.sponsorshipToyota Research Institute (Grant LP-C000765-SR)en_US
dc.language.isoen
dc.publisherInternational Joint Conferences on Artificial Intelligenceen_US
dc.relation.isversionofhttps://dx.doi.org/10.24963/IJCAI.2018/648en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleVariable-Delay Controllabilityen_US
dc.typeArticleen_US
dc.identifier.citationBhargava, Nikhil, Christian Muise and Brian Williams. “Variable-Delay Controllability.” In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, July 13-19, 2018, International Joint Conferences on Artificial Intelligence, pp. 4660-4666 © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligenceen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-11-04T13:53:13Z
dspace.date.submission2019-11-04T13:53:18Z
mit.journal.volume2018en_US
mit.metadata.statusComplete


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