{"id":232311,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232311","sets":["6164:6805:6807:11586"]},"path":["11586"],"owner":"44499","recid":"232311","title":["モンテカルロ木探索を用いたハゲタカのえじきエージェントの評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-01-06"},"_buckets":{"deposit":"531a981c-cf7e-4c3d-b815-daf1b6213dc2"},"_deposit":{"id":"232311","pid":{"type":"depid","value":"232311","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"モンテカルロ木探索を用いたハゲタカのえじきエージェントの評価","author_link":["628291","628292","628293"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"モンテカルロ木探索を用いたハゲタカのえじきエージェントの評価"},{"subitem_title":"Evaluation of \"Hol's der Geier\" agents using Monte Carlo Tree Search","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"モンテカルロ木探索, ハゲタカのえじき, 同時手番ゲーム","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2023-01-06","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"長崎県立大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"University of Nagasaki","subitem_text_language":"en"}]},"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/232311/files/IPSJ-WPRO2023021.pdf","label":"IPSJ-WPRO2023021.pdf"},"date":[{"dateType":"Available","dateValue":"2023-01-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-WPRO2023021.pdf","filesize":[{"value":"860.4 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"241142b3-69ac-4c46-a318-4ef32e8753d1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"志村, 伊生六"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山口, 文彦"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Izumu, Shimura  Fumihiko Yamaguchi","creatorNameLang":"en"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"モンテカルロ木探索は、シミュレーションを用いることで、ゲームにおける最善手を効率良く求めることができ、逐次手番ゲームにおける有効なアルゴリズムとして普及している。これを、行動を同時に選択する同時手番ゲームに用いる場合は、自分の行動を決定する際に他プレイヤの行動を観察することが出来ないため、逐次手番ゲームとは異なり、プレイヤ毎に求めた評価値をまとめた上でノードを選ぶ。本研究では、ハゲタカのえじきという同時手番ゲームの2人プレイで、モンテカルロ木探索の複数のバリエーションを用いた対戦エージェントを制作し、対戦における強さはどの程度か、理想的な戦略と考えられるナッシュ均衡戦略にどのくらい近づけるかを調べる。またハゲタカのえじきの3人プレイにおいて、2人プレイのエージェントに用いたアルゴリズムを基にモンテカルロ木探索の手法を提案し、対戦における強さを調べる。","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"138","bibliographic_titles":[{"bibliographic_title":"第64回プログラミング・シンポジウム予稿集"}],"bibliographicPageStart":"129","bibliographicIssueDates":{"bibliographicIssueDate":"2023-01-06","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T10:28:59.684408+00:00","created":"2025-01-19T01:33:06.163350+00:00","links":{}}