{"id":209860,"created":"2025-01-19T01:11:08.575264+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00209860","sets":["1164:5305:10533:10534"]},"path":["10534"],"owner":"44499","recid":"209860","title":["将棋のPV-MCTSに向けた深層学習モデルの最適化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-02-26"},"_buckets":{"deposit":"dcde63ec-ec13-416d-ad9d-a71099bbe343"},"_deposit":{"id":"209860","pid":{"type":"depid","value":"209860","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"将棋のPV-MCTSに向けた深層学習モデルの最適化","author_link":["530174","530175"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"将棋のPV-MCTSに向けた深層学習モデルの最適化"},{"subitem_title":"Optimization of deep learning model for PV-MCTS in Shogi","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"強いAIプレイヤ1","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-02-26","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岡山県立大学情報工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Okayama Prefectural University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/209860/files/IPSJ-GI21045011.pdf","label":"IPSJ-GI21045011.pdf"},"date":[{"dateType":"Available","dateValue":"2023-02-26"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-GI21045011.pdf","filesize":[{"value":"1.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"18"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"48ccfd36-0a68-4b2c-952a-f7c31815a70e","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"芝, 世弐"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Seiji, Shiba","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11362144","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8736","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"AlphaZeroの名声により深層学習モデルを用いたPV-MCTSによる将棋プログラムは従来のαβ型のプログラムと同等に戦える可能性が示された．しかしながら，短時間の学習時間という点に注目されているがその作成プロセスやモデル・アルゴリズム等の選定過程はあまり明らかにされていない．本研究では将棋のPV-MCTSに適した深層学習モデルとはどのようなものが望ましいのか，またどうすれば効率的に強化できるのかについて検討を行った．2020年度第一回電竜戦にて活躍した二番絞りプレミアムに関する成果報告である．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Due to the fame of AlphaZero, it was shown that the Shogi program by PV-MCTS with deep learning model can compete with the conventional αβ search program. However, although attention is paid to the short learning time in AlphaZero, the creation process and selection process of the deep learning model and algorithm have not been clarified very much. In this study, we investigated what kind of deep learning model is suitable for Shogi PV-MCTS, and how to learn efficiently. This is a report on the results of the nibanshibori premium in the first Denryusen in 2020.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告ゲーム情報学（GI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-02-26","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicVolumeNumber":"2021-GI-45"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T18:22:00.274329+00:00","links":{}}