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AITWolf
愛知工業大学
福井敬徳
エージェントの特徴
雑談をする
勝利に貢献しない会話の発言
発言された話題の特定
分類する話題
レーベンシュタイン距離
標本データ
雑談
勝利に貢献しない発言
挨拶
相槌
追放された人、食べられた人への
発言
自分の考え、意見、戦略
雑談
自然言語処理を必要としない情報を
条件に発言
生きているプレイヤー
ゲーム内日時
その日の会話回数
発言された話題の特定
分類する話題
Comingout(役職の公開)
Vote(投票に関する発話)
Divined(占い結果)
レーベンシュタイン距離
2つの文字列の距離を測る
私は占い師です 占い師COします
20%似てる
私は占い師です
発言された話題の特定
標本データ
特定の話題に分類されるべき文
のデータ群
占い師COします
私は占い師です
私は占い師です
標本データ
⬇︎ 発言
⬇︎
20%似てる
Divined
標本データ
発言された話題の特定
分類する話題の数だけ標本デー
タを作成
どの話題にも分類されないこと
もある
私は占い師です
俺が狂人だ!
・
・
・
占い師COします
Vote
標本データ
Comingout
標本データ
距離の測定
まとめ
戦略らしいこと
投票されそうだったら、疑った
人を投票する
占い師は投票しない
占い師の時対抗COが来たら投票
プロトコル部門との違い
雑談が楽しめる
何を話しているのかわからない

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Gat2017-AITWolfアルゴリズム紹介

Hinweis der Redaktion

  1. チーム名AITWolfです. 作成したエージェントについて説明します.
  2. 作成したエージェントの特徴は2つです 1つは雑談をおこなうこと 雑談とは,直接,勝利に貢献しない会話のことです。 「おはよう」などの挨拶、「あぁ,なるほど,そうだね」などの相槌が含まれます. 2つ目は発言された話題の特定を行うことです 相手が何について話しているかを分類をします. 単純に文字列検索を行わず,レーベンシュタイン距離と標本データを使って話題の分類をします. 詳しい話は後にします
  3. 雑談とは,勝利に貢献しない発言です. 雑談には,挨拶,相槌,追放された人や食べられた人への発言, 自分の考え,意見,戦略をおこないます.
  4. 雑談するトリガーとなるのは,自然言語処理を必要としない情報です. 例えば,生きているプレイヤーの中から適当に疑ってみたり、ゲーム内日時から、1日目は挨拶を2日目には「まだ狂人いるかも」などを話します.その他にも,その日の会話回数などをトリガーに用意した発話を発言します
  5. 作成したエージェントでは発言された話題の特定をおこないます. 特定する話題は3つ,comingout vote divinedです これらの話題の特定にはレーベンシュタイン距離を用います. レーベンシュタイン距離の詳しい話は時間の都合上割愛します. ここでは、2つの文字列がどれだけ似ているかを数値化するメソッドだと思ってください.
  6. 例えばComingoutであるか特定したい場合,Comingoutの標本データを用意します. 標本データとは,特定の話題に分類されるべき文のデータ群です. 標本データ中の文と発言された文の距離がある程度近いと,Comingoutの話題であると特定します.
  7. 標本データを分類する話題の数だけ作成し,総当たりで距離を測定することで話題の特定を行います. 標本データとの距離が一定の閾値を超えない場合は,UNKNOWN話題に分類します. ほとんどの会話はUNKNOWN話題です.
  8. 戦略らしいことはあまりしていません, 自分に投票の話題が来てるときは投票し返す, 占い師は投票しない, 占い師の時に対抗COされたら投票する など基本的なことはします プロトコル部門と違って雑談が楽しめるのが自然言語処理部門の特徴だと思います. 反対に,話すことが規定に沿ってないため,何を話しているのか,戦略可能なレベルまで理解させることが課題だと思いました. 以上でエージェントの説明を終わります