{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00080931","sets":["1164:5305:6705:6706"]},"path":["6706"],"owner":"10","recid":"80931","title":["Cross-Entropy Methodを用いたコンピュータ将棋における探索パラメータの学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-02-24"},"_buckets":{"deposit":"a6f0c8bf-0aaa-49d0-9d45-c18c66dec50b"},"_deposit":{"id":"80931","pid":{"type":"depid","value":"80931","revision_id":0},"owners":[10],"status":"published","created_by":10},"item_title":"Cross-Entropy Methodを用いたコンピュータ将棋における探索パラメータの学習","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Cross-Entropy Methodを用いたコンピュータ将棋における探索パラメータの学習"},{"subitem_title":"Learning of Search Parameters using Cross-Entropy Method in Computer Shogi","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2012-02-24","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京農工大学大学院工学府"},{"subitem_text_value":"東京農工大学工学研究院"},{"subitem_text_value":"東京農工大学工学研究院"}]},"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/80931/files/IPSJ-GI12027003.pdf"},"date":[{"dateType":"Available","dateValue":"2014-02-24"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-GI12027003.pdf","filesize":[{"value":"947.0 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":"18"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"ccda1d35-047a-40f3-b4a7-346c90df132c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2012 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松原, 徹"},{"creatorName":"古宮, 嘉那子"},{"creatorName":"小谷, 善行"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Toru, Matsubara","creatorNameLang":"en"},{"creatorName":"Kanako, Komiya","creatorNameLang":"en"},{"creatorName":"Yoshiyuki, Kotani","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"将棋やチェスなどの思考ゲームが強いプログラムを作るためには，評価関数の精度と効率的な局面探索が必要であるとされている．複雑な評価関数のパラメータが機械学習に成功していることに比べ，静止探索の深さや Futility Pruning のマージンなどのパラメータは手動で決められていることが多く，最適な値であるとは言えない．そこで本研究ではこれらのパラメータを Cross-Entropy Method を用いて学習する手法を提案した．実験の結果，静止探索の深さや Aspiration 探索のウインドウ幅などのパラメータが良いと思われる値に収束した．また，学習したパラメータは人手による調整のパラメータに対して大きく勝ち越した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"It is considered that the accuracy of a evaluation function and efficient game-tree search are necessary to make a strong program of shogi and chess. Compared with the parameters of a complicated evaluation function, which have succeeded in machine learning, the searching parameters, such as the depth of quiescence search and a margin of Futility Pruning, are decided manually in many cases, and are not the optimal values. In this paper, we propose to apply the Cross-Entropy Method for learning these parameters. From the result of the experiment, some parameters converged as the value that seems to be good.Moreover, the learned parameters-based greatly won to the manual parameters-based.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告ゲーム情報学（GI）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2012-02-24","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2012-GI-27"}]},"relation_version_is_last":true,"weko_creator_id":"10"},"id":80931,"updated":"2025-01-21T19:34:26.539462+00:00","links":{},"created":"2025-01-18T23:35:16.440892+00:00"}