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Wu, Dawen, Lisser, Abdel (2023) CCGnet: A deep learning approach to predict Nash equilibrium of chance-constrained games. Information Sciences, 627. 20-33 doi:10.1016/j.ins.2023.01.064

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Reference TypeJournal (article/letter/editorial)
TitleCCGnet: A deep learning approach to predict Nash equilibrium of chance-constrained games
JournalInformation Sciences
AuthorsWu, DawenAuthor
Lisser, AbdelAuthor
Year2023 (May)Volume627
PublisherElsevier BV
DOIdoi:10.1016/j.ins.2023.01.064Search in ResearchGate
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Mindat Ref. ID15676565Long-form Identifiermindat:1:5:15676565:9
GUID0
Full ReferenceWu, Dawen, Lisser, Abdel (2023) CCGnet: A deep learning approach to predict Nash equilibrium of chance-constrained games. Information Sciences, 627. 20-33 doi:10.1016/j.ins.2023.01.064
Plain TextWu, Dawen, Lisser, Abdel (2023) CCGnet: A deep learning approach to predict Nash equilibrium of chance-constrained games. Information Sciences, 627. 20-33 doi:10.1016/j.ins.2023.01.064
In(2023) Information Sciences Vol. 627. Elsevier BV

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