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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":"2759-4408","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"カーリングは氷上で行われるスポーツであり，2 つのチームがストーンと呼ばれる石を交互に投げ，最終的な総得点を競う．カーリングは戦略が勝敗に大きく影響することが知られている．この戦略の評価を行うために，コンピュータ上でカーリングをシミュレートするデジタルカーリングというプラットフォームが存在する．カーリング AI の学習のためにはシート上のストーン配置，エンドや得点差などの試合の情報が必要である．先行研究においては，学習データとして経験則から設計したアルゴリズムに基づいて自動生成したストーン配置や，カーリング AI 同士の自己対戦によって得られた試合情報を利用している．しかし，そのように自動生成した局面が現実の試合の局面とかけ離れている，多様性に欠けている，といった点で学習データとしては不十分な面もあり，検討が必要である．そこで，本研究では現実のカーリングの試合情報を活用することで，より多様で現実に即したデータにおいてカーリング AI モデルを学習させることを目指した．今回はノーティックルール適用後の 7 大会の Results Book を利用した．結果として 532 試合，74857 の局面を取得し，データベースに格納した．実際にこのデータベースを用いて，各エンドの最終投前の局面を取り出しモデル学習用の教師データを作成することができた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2","bibliographic_titles":[{"bibliographic_title":"研究報告スポーツ情報学（SI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-14","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"40","bibliographicVolumeNumber":"2024-SI-1"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":235023,"updated":"2025-01-19T09:38:17.290793+00:00","links":{}}