{"created":"2025-01-19T00:54:48.657494+00:00","updated":"2025-01-20T01:53:36.743730+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00188686","sets":["6504:9465:9481"]},"path":["9481"],"owner":"6748","recid":"188686","title":["マルチエージェント探索問題における粗視化とフィルタリングの統合手法による領域分割について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-03-13"},"_buckets":{"deposit":"776b62fa-1f08-41f0-8fd9-6ce6f7f2e7a5"},"_deposit":{"id":"188686","pid":{"type":"depid","value":"188686","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"マルチエージェント探索問題における粗視化とフィルタリングの統合手法による領域分割について","author_link":["428261","428259","428260"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"マルチエージェント探索問題における粗視化とフィルタリングの統合手法による領域分割について"}]},"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":"2018-03-13","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":"早大"},{"subitem_text_value":"早大"},{"subitem_text_value":"早大"}]},"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/188686/files/IPSJ-Z80-2Q-04.pdf","label":"IPSJ-Z80-2Q-04.pdf"},"date":[{"dateType":"Available","dateValue":"2018-05-07"}],"format":"application/pdf","filename":"IPSJ-Z80-2Q-04.pdf","filesize":[{"value":"198.9 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"46900135-9225-426b-a699-6c09379ecd4a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"湯德, 尊久"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"杉山, 歩未"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菅原, 俊治"}],"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":"本研究では,マルチエージェント探索問題において,回収に複数体のエージェント が必要な対象物が複数種類ある環境を想定する.探索問題や追跡問題では,環境が複雑で大規模なほど,学習のための状態空間が増加し,学習が困難となる.我々は,エージェントの視野から得られる情報を制限,さらに粗視化を用いて状態空間を抽象化することで学習の効率を高く維持する手法を提案した.本稿では,本提案手法を用いた場合,探索領域を分割し担当領域を設定した場合としなかった場合とを比較して,全体の探索効率への影響を調査,評価する","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"350","bibliographic_titles":[{"bibliographic_title":"第80回全国大会講演論文集"}],"bibliographicPageStart":"349","bibliographicIssueDates":{"bibliographicIssueDate":"2018-03-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2018"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"id":188686,"links":{}}