{"updated":"2025-01-20T02:42:30.785607+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00186102","sets":["1164:5305:9416:9417"]},"path":["9417"],"owner":"11","recid":"186102","title":["弾幕シューティングゲームを対象とした汎用的学習法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-02-23"},"_buckets":{"deposit":"5dc48638-c6e0-4799-84b6-720fbfe8ca3a"},"_deposit":{"id":"186102","pid":{"type":"depid","value":"186102","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"弾幕シューティングゲームを対象とした汎用的学習法","author_link":["416138","416141","416140","416139"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"弾幕シューティングゲームを対象とした汎用的学習法"},{"subitem_title":"General machine learning for Bullet Hell Game","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"一人で遊ぶゲーム","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-02-23","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"松江工業高等専門学"},{"subitem_text_value":"松江工業高等専門学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"National Institute of Technology. Matsue College","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Technology. Matsue College","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/186102/files/IPSJ-GI18039004.pdf","label":"IPSJ-GI18039004.pdf"},"date":[{"dateType":"Available","dateValue":"2020-02-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-GI18039004.pdf","filesize":[{"value":"5.3 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":"26873558-7d0c-4917-9058-42a583091ae1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"野村, 直也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"橋本, 剛"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Naoya, Nomura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tusyoshi, Hashimoto","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":"近年のゲーム AI は AI 自身が評価関数を生成する汎用的な学習法が成果をあげており，代表的なものとして Deep Q Network (DQN) などがある．これらの手法は様々なゲームに適用可能であるが，画面の情報量が多いものや操作が複雑なゲームでは学習が進まないという問題があり，さらに広い範囲のゲームに適用できる手法が必要とされる．本研究では複雑なゲームの一つとして弾幕シューティングゲームを取り上げ，このゲームに適用可能な学習手法を提案することで，更に汎用的な手法について考察する．弾幕シューティングゲームにおける人間のプレイの性質に着目すると，人間は画面全体を見ておらず，初心者は視野が狭く，上達するに連れ視野が広くなっていくという性質があると考えた．ゲーム序盤は観測範囲が狭く，徐々に観測範囲が拡大していくという性質を学習システム内に組み込むことで複雑なゲームに適応させる．本研究では観測範囲を狭くして学習の効率化が図れるか実験を行い確認した．観測範囲を狭めて学習させたところ，従来の手法よりも高いスコアを獲得した．また，観測範囲の変化量の妥当性や，その他のゲームへの適用について考察を行った．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Deep Q Network (DQN) is applicable to various games, but it has a problem that it tends to fail in complicated and difficult games. In this research, we pick up barrage STG even among complicated games, and tried to succeed in learning by incorporating the characteristics of human play in this game into DQN. In this game, human has a feature that are not seeing the entire screen and change the field of vision according to the amount of bullets. By incorporating the mechanism into DQN, we obtained better results than the conventional method.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告ゲーム情報学（GI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2018-02-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"2018-GI-39"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:53:09.290997+00:00","id":186102,"links":{}}