{"_buckets": {"deposit": "03b7e852-6f43-46c4-8e78-b075c06db43e"}, "_deposit": {"id": "5326", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "5326"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00005326"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2005-11", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "680", "bibliographicPageStart": "679", "bibliographic_titles": [{"bibliographic_title": "17th IEEE International Conference on Tools with Artificial Intelligence"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "In this paper, we focus on behavioral decisions for empty transport veicles e.g., they shoud wait on their place or go to other places. Effective behaviors of empty vehicles enable to decrease waiting times of customers. Our algorithm to acquire such effective behaviors is based on transport experiences of vehicles (i.e., history). It means that vehicles adjust to their city environment by learning trends of transport demands (e.g., amounts and directions). Our algorithm consists of learning and abstraction stages. In the learning stage, vehicles acquire low-level rules of actions at an intersection. In the abstraction stage, vehicles acquire high-level rules of actions in a region. Finally, we report simulation results and show the effectiveness of our algorithm.", "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/2237/6919"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "http://dx.doi.org/10.1109/ICTAI.2005.45", "subitem_relation_type_select": "DOI"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright \u00a9 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\u2019s 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."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Mukai, Naoto"}], "nameIdentifiers": [{"nameIdentifier": "13564", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Watanabe, Toyohide"}], "nameIdentifiers": [{"nameIdentifier": "13565", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Jun, Feng"}], "nameIdentifiers": [{"nameIdentifier": "13566", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-19"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "ICTAI_05_679.pdf", "filesize": [{"value": "625.8 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 625800.0, "url": {"label": "ICTAI_05_679.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/5326/files/ICTAI_05_679.pdf"}, "version_id": "21e2be1b-2b7d-41f5-9a5d-421ccf5abff9"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Behavioral Decision Based on Abstraction of Pheromone Distribution for Transport Vehicles", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Behavioral Decision Based on Abstraction of Pheromone Distribution for Transport Vehicles"}]}, "item_type_id": "10", "owner": "1", "path": ["312/313/314"], "permalink_uri": "http://hdl.handle.net/2237/6919", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2006-09-11"}, "publish_date": "2006-09-11", "publish_status": "0", "recid": "5326", "relation": {}, "relation_version_is_last": true, "title": ["Behavioral Decision Based on Abstraction of Pheromone Distribution for Transport Vehicles"], "weko_shared_id": 3}