{"updated":"2025-01-23T02:55:50.308851+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00010300","sets":["581:612:620"]},"path":["620"],"owner":"1","recid":"10300","title":["行動価値に着目した学習分類子システムの改善：マルチエージェント強化学習への接近"],"pubdate":{"attribute_name":"公開日","attribute_value":"2006-05-15"},"_buckets":{"deposit":"aab607b2-e132-41f6-951d-4d5ad78838cc"},"_deposit":{"id":"10300","pid":{"type":"depid","value":"10300","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"行動価値に着目した学習分類子システムの改善：マルチエージェント強化学習への接近","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"行動価値に着目した学習分類子システムの改善：マルチエージェント強化学習への接近"},{"subitem_title":"Improvement of Learning Classifier System by Action-value Function toward Multi-agent Reinforcement Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"特集：マルチエージェントの理論と応用","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2006-05-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"ATR ネットワーク情報学研究所"},{"subitem_text_value":"ATR ネットワーク情報学研究所 東京工業大学大学院総合理工学研究科"},{"subitem_text_value":"ATR ネットワーク情報学研究所 京都大学情報学研究科"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"ATR Network Informatics Laboratory","subitem_text_language":"en"},{"subitem_text_value":"ATR Network Informatics Laboratory,Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"ATR Network Informatics Laboratory,Graduate School of Informatics, Kyoto University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/10300/files/IPSJ-JNL4705015.pdf"},"date":[{"dateType":"Available","dateValue":"2008-05-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL4705015.pdf","filesize":[{"value":"560.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4cf090ba-8819-4c7b-ba81-d8c8f2ea2c8d","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2006 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"井上, 寛康"},{"creatorName":"高玉, 圭樹"},{"creatorName":"下原, 勝憲"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hiroyasu, Inoue","creatorNameLang":"en"},{"creatorName":"Keiki, Takadama","creatorNameLang":"en"},{"creatorName":"Katsunori, Shimohara","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"これまでで最も改善された学習分類子システムであるXCS は，決定的状態遷移からなる環境でのみ正しく動作することが知られている．本論文では，決定的状態遷移環境よりも複雑なマルチエージェント環境でも利用できる学習分類子システムを目指し，適切な経験の一般化が可能なXCS-QT を提案する．そしてその優位性をシミュレーション実験により示す．具体的には木の問題および追跡問題を用いて実験し，マルチエージェント環境はXCS にとって正しく動作できないいくつかの要因が含まれていること，およびXCS-QT がそれら要因を克服することを示す．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"XCS is the newest Learning Classifier System (LCS), and at present it can only be used for deterministic transition environments. This paper proposes XCS-QT as a modified LCS that can appropriately generalize its experience and can be used for multi-agent environments that are more complex than deterministic transition environments. We then show the system’s advantage via simulation experiments using quasi-tree problems and hunter problems. Through the experiments, we demonstrate that there are several reasons why XCS cannot work very well in multi-agent environments, and that XCS-QT can overcome those problems.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1492","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1483","bibliographicIssueDates":{"bibliographicIssueDate":"2006-05-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"47"}]},"relation_version_is_last":true,"item_2_alternative_title_2":{"attribute_name":"その他タイトル","attribute_value_mlt":[{"subitem_alternative_title":"エージェント学習システム"}]},"weko_creator_id":"1"},"created":"2025-01-18T22:45:14.914559+00:00","id":10300,"links":{}}