{"updated":"2025-01-19T12:45:11.514207+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00225377","sets":["581:11107:11112"]},"path":["11112"],"owner":"44499","recid":"225377","title":["遺伝的プログラミングを用いたモンテカルロガイスターのプレイアウト方策生成"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-03-15"},"_buckets":{"deposit":"8c3505ef-5205-4de0-be18-49834b250a5d"},"_deposit":{"id":"225377","pid":{"type":"depid","value":"225377","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"遺伝的プログラミングを用いたモンテカルロガイスターのプレイアウト方策生成","author_link":["596168","596167","596169","596170"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"遺伝的プログラミングを用いたモンテカルロガイスターのプレイアウト方策生成"},{"subitem_title":"Generating Playout Policy of Monte Carlo Geister Using Genetic Programming","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:若手研究者] 不完全情報ゲーム，ゲーム木探索，遺伝的プログラミング","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2023-03-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"高知工科大学情報学群"},{"subitem_text_value":"株式会社ゼンリン"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"School of Informatics, Kochi University of Technology","subitem_text_language":"en"},{"subitem_text_value":"Zenrin Co. Ltd.","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/225377/files/IPSJ-JNL6403015.pdf","label":"IPSJ-JNL6403015.pdf"},"date":[{"dateType":"Available","dateValue":"2025-03-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6403015.pdf","filesize":[{"value":"706.6 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"23c47139-44e6-4389-8455-b1206c3ce37c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"竹内, 聖悟"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"栃川, 純平"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shogo, Takeuchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Junpei, Tochikawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ガイスターは，相手の駒の色が分からない二人不完全情報ゲームである．ガイスターにはモンテカルロ法ベースのプレイヤがあり，プレイアウト（シミュレーション）にはランダム方策が用いられている．完全情報ゲームのプレイアウト方策としては，ゲームの知識を用いたルールベース方策や機械学習によって生成した方策がランダム方策よりも性能を向上させる．しかし相手の情報を部分的にしか知ることのできない不完全情報ゲームにおいて，その未知の情報に基づいた方策が有効であるかは不明である．また，知識の導入には人間がゲームに習熟している必要があるが，人間のゲーム習熟度によらずにプレイアウト方策を作成できることが望ましい．本研究では，このような問題の解決のため遺伝的プログラミングを用いた方策作成を提案する．ゲームへの習熟が不要となることと適切な適応度を設定できれば方策による性能改善が期待できることが利点としてあげられる．また，用いる知識として未知の情報を含む場合とそうでない場合とで実験を行い，未知の情報の利用が性能改善に貢献するかを確認する．モンテカルロ法ベースのガイスタープレイヤを対象とした実験結果から，提案手法によりランダム方策よりも強い方策が生成できること，未知の情報を用いて性能が高くなることを確認し，提案手法の有効性を示した．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Geister is a two-player imperfect information game in which the color of the opponent's pieces is unknown. In Geister, the Monte Carlo player use a random policy in playout. The rule-based policy using human knowledge is considered stronger than the random policy. However, it is unclear whether rule-based policy based on unknown information is effective in imperfect information game, where only partial information about the opponent is available. In addition, it is desirable to be able to create playout independent of human game proficiency, although human players must be proficient in the game to introduce knowledge. In this research, we propose a method to generate playout policy using genetic programming to solve those problems. We conduct the experiments on Monte-Carlo Geister's playout in order to confirm the effectiveness of the proposed method. Experimental results show that the proposed method can generate stronger playout policy than random policy, and that the performance can be improved by using unknown information.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"716","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"708","bibliographicIssueDates":{"bibliographicIssueDate":"2023-03-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"64"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00225268","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"created":"2025-01-19T01:24:53.345341+00:00","id":225377,"links":{}}