{"updated":"2025-01-20T04:43:16.645140+00:00","links":{},"id":180991,"created":"2025-01-19T00:48:47.071344+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00180991","sets":["6504:9168:9182"]},"path":["9182"],"owner":"6748","recid":"180991","title":["ゲームの機械学習に用いる対戦譜の抽出方法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-03-16"},"_buckets":{"deposit":"0ca25c0b-062f-4e91-9ebc-a6f4a836008d"},"_deposit":{"id":"180991","pid":{"type":"depid","value":"180991","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"ゲームの機械学習に用いる対戦譜の抽出方法","author_link":["391514","391513"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ゲームの機械学習に用いる対戦譜の抽出方法"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2017-03-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電通大"},{"subitem_text_value":"電通大"}]},"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/180991/files/IPSJ-Z79-6P-01.pdf","label":"IPSJ-Z79-6P-01.pdf"},"date":[{"dateType":"Available","dateValue":"2017-05-22"}],"format":"application/pdf","filename":"IPSJ-Z79-6P-01.pdf","filesize":[{"value":"215.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"b635550d-c6f1-4898-b661-3162b636a76f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"石川, 純平"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中山, 泰一"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"将棋やチェスなどの人工知能(AI)については古くから研究され、現在これらゲームのAIはプロの人間を打ち負かすまでに至っている。将棋はプロ棋士の対局の棋譜が数多く存在し、機械学習の教師データが不足しない点で研究題材として優れているが、例えば「駒落ち」「10分切れ負け」といった特殊なルールでは十分な数のプロ棋士の棋譜が無いため、一般のプレイヤの棋譜を教師データとして用いる必要が出てくる。しかし、こうした棋譜にはプロ棋士の棋譜と違い、教師データとして適切でないものが数多く含まれると考えられる。本研究では、この「適切でない棋譜」がどれほどの割合で存在するのか調べ、これを抽出して機械学習の教師データから排することで、どの程度AIに影響があるのかを検証した。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"460","bibliographic_titles":[{"bibliographic_title":"第79回全国大会講演論文集"}],"bibliographicPageStart":"459","bibliographicIssueDates":{"bibliographicIssueDate":"2017-03-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"6748"}}