{"created":"2021-03-01T06:11:52.952253+00:00","id":5325,"links":{},"metadata":{"_buckets":{"deposit":"3b37d083-760d-4ec0-9c5f-20ca92640378"},"_deposit":{"id":"5325","owners":[],"pid":{"revision_id":0,"type":"depid","value":"5325"},"status":"published"},"_oai":{"id":"oai:nagoya.repo.nii.ac.jp:00005325","sets":["312:313:314"]},"author_link":["13562","13563"],"item_10_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2005-11","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"57","bibliographicPageStart":"51","bibliographic_titles":[{"bibliographic_title":"17th IEEE International Conference on Tools with Artificial Intelligence","bibliographic_titleLang":"en"}]}]},"item_10_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Route planning is one of the important tasks for transport systems. Appropriate policies  for route selections improve not only profitability of transport companies but also convenience of customers. Traditional ways for establishing the policies depend on manual efforts based on statistical data of transports. Moreover, traditional route planning techniques are reactive, i.e., an optimization based on information provided in advance. It is difficult for the manual policies and the reactive planning techniques to adjust dynamic changes of transport trends for customers such as amount and direction of transport demands, i.e., drivers of transport vehicles must follow the policies provided in advance. Therefore, in this paper we show how the proactive route planning based on expected rewards for transport systems can be modeled as a reinforcement learning problem. And, we show how agents as transport vehicles acquire their policies for route selection autonomously and dynamically. The leaning ability of transport trends enables transport vehicles to foresee the next destination which provides high rewards. Finally, we report simulation results and make the effectiveness of our proposal strategy clear.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10_identifier_60":{"attribute_name":"URI","attribute_value_mlt":[{"subitem_identifier_type":"HDL","subitem_identifier_uri":"http://hdl.handle.net/2237/6918"}]},"item_10_publisher_32":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IEEE","subitem_publisher_language":"en"}]},"item_10_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isVersionOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1109/ICTAI.2005.101","subitem_relation_type_select":"DOI"}}]},"item_10_rights_12":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"Copyright © 2005 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.","subitem_rights_language":"en"}]},"item_10_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_10_text_14":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_text_value":"application/pdf"}]},"item_1615787544753":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Mukai, Naoto","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"13562","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Watanabe, Toyohide","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"13563","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2018-02-19"}],"displaytype":"detail","filename":"ICTAI_05_51.pdf","filesize":[{"value":"1.6 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"ICTAI_05_51.pdf","objectType":"fulltext","url":"https://nagoya.repo.nii.ac.jp/record/5325/files/ICTAI_05_51.pdf"},"version_id":"50052e20-98eb-490b-8580-dd568c0d0d8c"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Proactive Route Planning Based on Expected Rewards for Transport Systems","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Proactive Route Planning Based on Expected Rewards for Transport Systems","subitem_title_language":"en"}]},"item_type_id":"10","owner":"1","path":["314"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2006-09-11"},"publish_date":"2006-09-11","publish_status":"0","recid":"5325","relation_version_is_last":true,"title":["Proactive Route Planning Based on Expected Rewards for Transport Systems"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-01-16T03:49:58.879564+00:00"}