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hal.structure.identifier
dc.contributor.authorOsanlou, Kevin
hal.structure.identifier
dc.contributor.authorFranck, Jeremy
hal.structure.identifierValeo.ai
dc.contributor.authorBursuc, Andrei
HAL ID: 3798
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorCazenave, Tristan
HAL ID: 743184
hal.structure.identifierCentre de recherche des écoles de Saint-Cyr Coëtquidan [Guer] [CREC]
dc.contributor.authorJacopin, Eric
hal.structure.identifierSAFRAN [Paris]
dc.contributor.authorGuettier, Christophe
ORCID: 0000-0002-1655-4898
hal.structure.identifier
dc.contributor.authorBenton, J.
dc.date.accessioned2023-10-04T11:40:25Z
dc.date.available2023-10-04T11:40:25Z
dc.date.issued2022
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/24978
dc.language.isoenen
dc.subjectPlanning, Routing, And Scheduling (PRS)en
dc.subjectSearch And Optimization (SO)en
dc.subjectMachine Learning (ML)en
dc.subject.ddc006.3en
dc.titleSolving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networksen
dc.typeCommunication / Conférence
dc.description.abstractenScheduling under uncertainty is an area of interest in artificial intelligence. We study the problem of Dynamic Controllability (DC) of Disjunctive Temporal Networks with Uncertainty (DTNU), which seeks a reactive scheduling strategy to satisfy temporal constraints in response to uncontrollable action durations. We introduce new semantics for reactive scheduling: Time-based Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We present a tree search approach to determine whether or not a DTNU is R-TDC. Moreover, we leverage the learning capability of a Graph Neural Network (GNN) as a heuristic for tree search guidance. Finally, we conduct experiments on a known benchmark on which we show R-TDC to retain significant completeness with regard to DC, while being faster to prove. This results in the tree search processing fifty percent more DTNU problems in R-TDC than the state-of-the-art DC solver does in DC with the same time budget. We also observe that GNN tree search guidance leads to substantial performance gains on benchmarks of more complex DTNUs, with up to eleven times more problems solved than the baseline tree search.en
dc.identifier.citationpages9877-9885en
dc.relation.ispartoftitleProceedings of the AAAI Conference on Artificial Intelligence, 36(9)en
dc.relation.ispartofpublnameAssociation for the Advancement of Artificial Intelligence (AAAI)en
dc.relation.ispartofdate2022-06
dc.relation.ispartofpages10416en
dc.relation.ispartofurlhttps://ojs.aaai.org/index.php/AAAI/issue/view/519en
dc.identifier.urlsitehttps://ojs.aaai.org/index.php/AAAI/article/view/21224en
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-1-57735-876-3en
dc.relation.conftitleThirty-Sixth AAAI Conference on Artificial Intelligenceen
dc.relation.confdate2022-03
dc.relation.confcityVirtuelen
dc.relation.confcountryUnited Statesen
dc.relation.forthcomingnonen
dc.identifier.doi10.1609/aaai.v36i9.21224en
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2023-10-04T11:33:02Z
hal.export.arxivnonen
hal.export.pmcnonen
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