LSTM Path-Maker: a new LSTM-based strategy for the multi-agent patrolling

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dc.contributor.authorOthmani-Guibourg, Mehdi
dc.contributor.authorFarges, Jean-Loup
dc.contributor.authorEl Fallah Seghrouchni, Amal
dc.date.accessioned2019-01-02T23:43:37Z
dc.date.available2019-01-02T23:43:37Z
dc.date.issued2019-01-08
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.076
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59502
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEmerging Issues in Distributed Group Decision-Making: Opportunities and Challenges
dc.subjectCollaboration Systems and Technologies
dc.subjectArtificial Neural Networks, Long Short-Term Memory, Multi-agent Coordinating, Multi-agent patrolling, Multi-agent systems
dc.titleLSTM Path-Maker: a new LSTM-based strategy for the multi-agent patrolling
dc.typeConference Paper
dc.type.dcmiText

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Emerging Issues in Distributed Group Decision-Making: Opportunities and Challenges