{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232892","sets":["1164:5305:11555:11556"]},"path":["11556"],"owner":"44499","recid":"232892","title":["Decoding Virtual Strategies: Deep Neural Network-driven Prediction of Player Movement via In-Game Location Data"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"f653a2f5-0b2f-4834-8376-1ff176680f83"},"_deposit":{"id":"232892","pid":{"type":"depid","value":"232892","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Decoding Virtual Strategies: Deep Neural Network-driven Prediction of Player Movement via In-Game Location Data","author_link":["631448","631452","631446","631445","631449","631451","631450","631447"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Decoding Virtual Strategies: Deep Neural Network-driven Prediction of Player Movement via In-Game Location Data"},{"subitem_title":"Decoding Virtual Strategies: Deep Neural Network-driven Prediction of Player Movement via In-Game Location Data","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2024-03-01","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/232892/files/IPSJ-GI24051004.pdf","label":"IPSJ-GI24051004.pdf"},"date":[{"dateType":"Available","dateValue":"2026-03-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-GI24051004.pdf","filesize":[{"value":"1.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"18"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"80a45d9d-7e9f-4dee-a259-57bbec85ce53","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Mhd, Irvan"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Franziska, Zimmer"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryosuke, Kobayashi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Rie, Shigetomi Yamaguchi"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Mhd, Irvan","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Franziska, Zimmer","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryosuke, Kobayashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Rie, Shigetomi Yamaguchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11362144","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8736","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"In modern video game design, understanding player movement is pivotal for creating immersive gaming experiences. This research delves into the realm of predictive modeling by leveraging in-game location data and trajectory information. Our approach employs a Deep Neural Network (DNN) model, designed to unravel intricate patterns in player behavior. Our research focuses on mapping virtual strategies through the DNN's predictive capabilities, shedding light on the complex dynamics inherent in player trajectories. By harnessing in-game location data, we demonstrate the effectiveness of our model in capturing player dynamics. This study not only contributes to the field of gaming analytics but also highlights the potential of deep learning in deciphering and predicting player behavior. The findings offer valuable insights into the cognitive aspects of gameplay, paving the way for more responsive and engaging virtual environments.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In modern video game design, understanding player movement is pivotal for creating immersive gaming experiences. This research delves into the realm of predictive modeling by leveraging in-game location data and trajectory information. Our approach employs a Deep Neural Network (DNN) model, designed to unravel intricate patterns in player behavior. Our research focuses on mapping virtual strategies through the DNN's predictive capabilities, shedding light on the complex dynamics inherent in player trajectories. By harnessing in-game location data, we demonstrate the effectiveness of our model in capturing player dynamics. This study not only contributes to the field of gaming analytics but also highlights the potential of deep learning in deciphering and predicting player behavior. The findings offer valuable insights into the cognitive aspects of gameplay, paving the way for more responsive and engaging virtual environments.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告ゲーム情報学（GI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"2024-GI-51"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":232892,"updated":"2025-01-19T10:17:14.796164+00:00","links":{},"created":"2025-01-19T01:34:01.410532+00:00"}