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  1. 学術雑誌掲載済論文
  2. 和雑誌

マルコフ決定過程のロールプレイングゲームへの適用

https://kitami-it.repo.nii.ac.jp/records/7887
9b6621f8-10f0-432a-a63f-adee82ef8516
名前 / ファイル ライセンス アクション
No197.pdf No197.pdf (599.8 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2015-08-17
タイトル
言語 ja
タイトル マルコフ決定過程のロールプレイングゲームへの適用
タイトル
言語 en
タイトル Applying Markov Decision Processes to Role-playing Game
言語
言語 jpn
キーワード
主題Scheme Other
主題 マルコフ決定過程
キーワード
主題Scheme Other
主題 ロールプレイングゲーム
キーワード
主題Scheme Other
主題 統計的決定理論
キーワード
主題Scheme Other
主題 Markov decision processes
キーワード
主題Scheme Other
主題 role-playing game
キーワード
主題Scheme Other
主題 statistical decision theory
資源タイプ
資源 http://purl.org/coar/resource_type/c_6501
タイプ journal article
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
その他のタイトル
その他のタイトル Applying Markov Decision Processes to Role-playing Game
著者 前田, 康成

× 前田, 康成

WEKO 273
KAKEN - 研究者検索 30422033

ja 前田, 康成

Search repository
後藤, 文太朗

× 後藤, 文太朗

WEKO 40687

ja 後藤, 文太朗

Search repository
升井, 洋志

× 升井, 洋志

WEKO 44866
KAKEN 30396345

ja 升井, 洋志

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桝井, 文人

× 桝井, 文人

WEKO 44864
KAKEN 80324549

ja 桝井, 文人

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鈴木, 正清

× 鈴木, 正清

WEKO 89671
KAKEN - 研究者検索 60192621

ja 鈴木, 正清

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著者別名
姓名
姓名 Maeda, Yasunari
言語 en
著者別名
姓名
姓名 Goto, Fumitaro
言語 en
著者別名
姓名
姓名 Masui, Hiroshi
言語 en
著者別名
姓名
姓名 Masui, Fumito
言語 en
著者別名
姓名
姓名 Suzuki, Masakiyo
言語 en
抄録
内容記述タイプ Abstract
内容記述 従来からマルコフ決定過程(MDP)を用いたロールプレイングゲーム(RPG)のモデル化が行われている.従来研究ではRPGが部分的にモデル化されている.本研究では,MDPを用いてより一般的なRPGのモデル化を行う.最初にMDPの真のパラメータ既知の場合に相当するRPGについて,報酬の期待値を最大にするアルゴリズムを提案する.次にMDPの真のパラメータ未知の場合に相当するRPGについて,ベイズ基準のもとで報酬を最大にするアルゴリズムを提案する.次にMDPの真のパラメータ未知の場合に相当するRPGについて,学習データを用いて報酬を近似的に最大にするアルゴリズムを提案する.In previous research a part of role-playing game (RPG) is represented with Markov decision processes (MDP). In this research we represent a more general PRG with MDP. We maximize an expected total reward under the condition that the true parameter of MDP is known in the first proposition. We maximize an expected total reward with respect to a Bayes criterion under the condition that the true parameter of MDP is unknown in the second proposition. We approximately maximize an expected total reward using learning data under the condition that the true parameter of MDP is unknown in the third proposition.
書誌情報 ja : 情報処理学会論文誌

巻 53, 号 6, p. 1608-1616, 発行日 2012-06
出版者
出版者 一般社団法人 情報処理学会
著者版フラグ
値 publisher
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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