Please use this identifier to cite or link to this item: https://dspace.ncfu.ru/handle/20.500.12258/3066
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dc.contributor.authorHolldobler, S.-
dc.date.accessioned2018-09-25T12:50:11Z-
dc.date.available2018-09-25T12:50:11Z-
dc.date.issued2017-
dc.identifier.citationHölldobler, S., Möhle, S., Tigunova, A. Lessons learned from AlphaGo // CEUR Workshop Proceedings. - 2017. - Volume 1837. - Pages 92-101ru
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85019836754&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=44d90154573ea1cc971b53e97e0ea554&sot=aff&sdt=sisr&sl=174&s=AF-ID%28%22North+Caucasus+Federal+University%22+60070541%29+OR+AF-ID%28%22Stavropol+State+University%22+60070961%29+OR+AF-ID%28%22stavropolskij+Gosudarstvennyj+Tehniceskij+Universitet%22+60026323%29&ref=%28Lessons+learned+from+AlphaGo%29&relpos=0&citeCnt=0&searchTerm=-
dc.identifier.urihttp://hdl.handle.net/20.500.12258/3066-
dc.description.abstractThe game of Go is known to be one of the most complicated board games. Competing in Go against a professional human player has been a long-standing challenge for AI. In this paper we shed light on the AlphaGo program that could beat a Go world champion, which was previously considered non-achievable for the state of the art AIru
dc.language.isoenru
dc.publisherCEUR-WSru
dc.relation.ispartofseriesCEUR Workshop Proceedings-
dc.subjectBoard gamesru
dc.subjectHuman playersru
dc.subjectState of the artru
dc.titleLessons learned from AlphaGoru
dc.typeСтатьяru
vkr.amountPages 92-101ru
vkr.instИнститут информационных технологий и телекоммуникаций-
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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