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        <datestamp>2025-01-19T23:15:56Z</datestamp>
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          <dc:title>少ない棋譜からの将棋プレイヤ棋力推定手法の提案</dc:title>
          <dc:title xml:lang="en">Shogi Player's Rating Estimation Method using Fewer Game Records</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>馬場, 匠</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>伊藤, 毅志</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Takumi, Baba</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Takeshi, Ito</jpcoar:creatorName>
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          <datacite:description descriptionType="Other">本研究では，なるべく少ない棋譜から将棋プレイヤの棋力を推定する手法を提案する．先行研究の将棋AIを用いた棋力推定の手法では20局程度の対戦を必要としていた．これは，分析対象とする局面の条件に「序盤や終盤の除外」などの制約があるため，1局あたりの分析局面数が少なることが原因である．本研究では，どのような局面が棋力推定に有効に働くのかを詳細に調べた．その結果，接戦の局面ほど棋力推定に適していることが判明した．そこで，接待将棋AIを用いて接戦の局面を多く作ることにより，少ない対局数から棋力推定をする手法を提案した．この手法により，3～4局程度の対戦でかなり正確に棋力推定ができることを確認した．</datacite:description>
          <datacite:description descriptionType="Other">In this research, we propose a method to estimate Shogi players' rating from fewer game records. In the related study of estimating players' rating using Shogi AI, about 20 game records were required. This is due to the fact that there are constraints such as "exclusion of opening game and end game" in the condition of the positions to be analyzed, so that the number of analysis positions per a game is very small. In this research, we investigated in detail what kind of position works effectively for rating estimation. As the result, we found that positions in close game are more suitable for rating estimation. Therefore, we proposed a method to estimate players' rating from a small number of games by creating many close game positions using an entertainment shogi AI. By using this method, we confirmed that we can estimate players' rating fairly accurately with 3 or 4 games.</datacite:description>
          <dc:publisher xml:lang="ja">情報処理学会</dc:publisher>
          <datacite:date dateType="Issued">2019-03-01</datacite:date>
          <dc:language>jpn</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_18gh">technical report</dc:type>
          <jpcoar:identifier identifierType="URI">https://ipsj.ixsq.nii.ac.jp/records/194974</jpcoar:identifier>
          <jpcoar:sourceIdentifier identifierType="ISSN">2188-8736</jpcoar:sourceIdentifier>
          <jpcoar:sourceIdentifier identifierType="NCID">AA11362144</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle>研究報告ゲーム情報学（GI）</jpcoar:sourceTitle>
          <jpcoar:volume>2019-GI-41</jpcoar:volume>
          <jpcoar:issue>13</jpcoar:issue>
          <jpcoar:pageStart>1</jpcoar:pageStart>
          <jpcoar:pageEnd>8</jpcoar:pageEnd>
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            <datacite:date dateType="Available">2021-03-01</datacite:date>
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