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DC Field | Value | Language |
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dc.contributor.author | Holldobler, S. | - |
dc.date.accessioned | 2018-09-25T12:50:11Z | - |
dc.date.available | 2018-09-25T12:50:11Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Hölldobler, S., Möhle, S., Tigunova, A. Lessons learned from AlphaGo // CEUR Workshop Proceedings. - 2017. - Volume 1837. - Pages 92-101 | ru |
dc.identifier.uri | https://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.uri | http://hdl.handle.net/20.500.12258/3066 | - |
dc.description.abstract | The 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 AI | ru |
dc.language.iso | en | ru |
dc.publisher | CEUR-WS | ru |
dc.relation.ispartofseries | CEUR Workshop Proceedings | - |
dc.subject | Board games | ru |
dc.subject | Human players | ru |
dc.subject | State of the art | ru |
dc.title | Lessons learned from AlphaGo | ru |
dc.type | Статья | ru |
vkr.amount | Pages 92-101 | ru |
vkr.inst | Институт информационных технологий и телекоммуникаций | - |
Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
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File | Description | Size | Format | |
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scopusresults 353 .pdf Restricted Access | 62.25 kB | Adobe PDF | View/Open |
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