{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236338","sets":["6504:11678:11688"]},"path":["11688"],"owner":"44499","recid":"236338","title":["模倣学習型ニューラルネットワークを活用した歩行流制御施策の最適化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"1f9b4db8-02e2-4c56-aa19-c1386063cd8a"},"_deposit":{"id":"236338","pid":{"type":"depid","value":"236338","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"模倣学習型ニューラルネットワークを活用した歩行流制御施策の最適化","author_link":["645978","645981","645982","645983","645980","645979"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"模倣学習型ニューラルネットワークを活用した歩行流制御施策の最適化"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2024-03-01","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":"阪大"},{"subitem_text_value":"阪大"},{"subitem_text_value":"阪大"},{"subitem_text_value":"阪大"},{"subitem_text_value":"阪大"},{"subitem_text_value":"NTTドコモ"}]},"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/236338/files/IPSJ-Z86-7D-03.pdf","label":"IPSJ-Z86-7D-03.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-04"}],"format":"application/pdf","filename":"IPSJ-Z86-7D-03.pdf","filesize":[{"value":"664.9 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"1e253c0a-586d-4723-8322-d784240bd355","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"田中, 福治"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"天野, 辰哉"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"内山, 彰"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"廣森, 聡仁"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山口, 弘純"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 佑輔"}],"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":"本論文では，大群衆が集まるイベント後の混雑を緩和する施策の最適化手法を提案する．提案手法では，ニューラルネットワークによるマルチエージェントシミュレータの代理モデルを活用した，勾配ベースのブラックボックス最適化手法を導入する．シミュレータとその評価関数を微分可能な関数として複製し，その勾配情報に基づいて施策を最適化することで，最適な施策の特定に要する時間を短縮する．評価実験から，提案手法により導出された施策はグリッドサーチで得られたものよりも高い精度を達成することを確認した．また，シミュレータを用いた施策探索と同等の精度を達成しつつ，より高速に施策探索が可能であることを確認した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"42","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"41","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":236338,"updated":"2025-01-19T09:17:59.382265+00:00","links":{},"created":"2025-01-19T01:38:20.075040+00:00"}