ノート Note ユーザの習熟度分析のためのコントローラ操作ログの抽出システムの提案 藏野晴基 1, 高橋翔太 1, 高橋幸雄 1 GamePad Operation Log Extraction System for User's Play Skill Level Analysis Haruki

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1 ノート Note 藏野晴基 1, 高橋翔太 1, 高橋幸雄 1 GamePad Operation Log Extraction System for User's Play Skill Level Analysis Haruki KURANO 1 Shouta TAKAHASHI 1 Yukio TAKAHASHI 1 要旨 ゲームにおいて状況に応じて勝利するためのスキルを獲得することは初心者にとって難しい作業である オンラインゲーム上達のためには長い時間練習をして攻略のコツを習得したり, コーチの指導を受けて学んだりするということが考えられるがこれらにはコストがかかるという問題がある 本研究はゲームのプレイログを抽出するシステムを開発し, 勝敗に寄与する特徴がユーザスキルによって異なるのかを分析した 分析の結果, ユーザスキルによって特徴的な行動に差があることが分かった Abstract: Video Game requires every player to acquire play skill in order to win the game. However, it is not easy for players to acquire play skill manually from game playing, due to various situations. We developed a gameped operation log extraction system for user's play skill analysis. The results of this system showed that the important factor according to the user's play skill and contexts. キーワード 操作ログ抽出, プレイスキル分析, ゲーム Keywords: log extraction, play skill analysis, video game. はじめにコンピュータゲームをスポーツ競技として捉える e sports が広い世代に普及している [1] 日本の子どもの遊びにおいてもおにごっこ, 球技よりもゲームで遊ぶ割合が多いことが分かっている [2] ゲームにおける難易度は遊びを楽しむ上での重要な要素であり, 難易度は高すぎても低すぎてもプレイするモチベーションに問題があると考えられる ユーザのプレイスキルは様々であり, 上達するためには各場面に応じてどんな行動が適切かを把握し, それを実行するための訓練が必要であるが, 全てのユーザがこういった状況分析や練習を行なえるわけではない [3] 我々はゲーム中のコントローラー操作を抽出するシステムを開発した 本稿は本システムを用いて実際のゲームプレイにおける初心者, 熟練者の操作を抽出し, 得られるゲームスキルの違いについて報告する. 関連研究 Fujii らは字幕付き動画を見ている視線の動きを追跡することで英語レベルを推定し, 各個人の英語レベルに適した英語学習を構築できるシステム SubMe を提案した[4] スポーツ分野では本研究に類似する研究がある 弓道の練習を支援するシステムとして, 正しい型の姿勢を視覚的に判別する研究がある [5] また歌の調子外れの治療支援システムの研究も行われている[6] これらの研究は正しいデータとのズレをユーザに提示するものである デジタルゲームではユーザとの対話が重要であり, 自然言語処理, 画像処理, 機械学習など様々な技術を用いて調整が行なわれている [7] e sports 分野の具体的な例 1 国士舘大学理工学部

2 では FPS における照準を支援する研究がある [8] 榊原らはユーザのプレイログを用いて,e sports においてプレイ中の状況を分析するシステムを開発した [9] 同様の研究ではサッカーにおいて相手のディフェンスライン, オフサイドラインを可視化することで現状の把握を支援する研究がある [10,11]. コントローラ操作ログ抽出システム本システムはゲームプレイ中のコントローラ操作ログを抽出するために次の 2 つのモジュールで構成される 本システムは任天堂 Switch を対象とした コントローラ操作とゲーム機と PC に出力するコンバータモジュール コンバータから得られる操作ログの出力モジュール コンバータモジュールでは Console Tuner から発売される Titan Two を用いた 図 1 に Titan Two の概要を示す 図 Titan Two コンバータ ( 製品マニュアルより引用 ) 図 gpc による操作ログサンプル

3 国士舘大学紀要 情報科学 第41号 2020年) 操作ログの出力モジュールでは コンバータから得られるバイナリ形式の操作ログ gmk を C 言語風 のスクリプト言語である gpc コードで変換するプログラムを作成し 操作ログを抽出した 図 2 に gpc コードの例を示す. 実 験 ここでは実際のゲームプレイにおける操作ログの抽出事例を説明する 実験では任天堂から発売されて いる大乱闘スマッシュブラザーズ SPECIAL2 を用いた このゲームではコンピュータと対戦する際にレ ベルを 最弱 9 最強 まで選ぶことができる 6 以下と対戦するのがちょうど良いユーザを初 心者 コンピュータのレベル 9 にも簡単に勝つことのできるユーザを熟練者と仮定し それぞれのコン トローラ操作を抽出した その結果を図 3 に示す 図 3 は全てのボタン スティック操作を表示しているため初心者と熟練者の違いが把握することは困 図 2 コントローラ操作ログ 上初心者下熟練者

4 図 キャラクターの移動に関する操作ログ ( 上初心者下熟練者 ) 難である 図 4 にキャラクターの操作に用いるスティックに関する操作ログのみを抽出した場合の結果を示す キャラクターの移動だけに着目して比較すると初心者は熟練者に比べ, 入力の波が荒くことから常に移動し続けていることが分かる それに対し熟練者は動いているときと止まっているときがあるということが明確に分かる. おわりに本研究はゲーム中の操作ログを分析し, プレイヤーのキー操作ログを抽出した 実験の結果, 初心者, 熟練者によって特徴的な動作があることを確認した 今後の課題として, 時系列分析手法を用いてプレイヤーごとの違いを統計的に算出すること, また他のゲームにおいての比較検討を行なうことが挙げられる また本研究で開発したソフトウェアはオープンソースソフトウェアとして公開予定である 参考文献 [ 1 ] e sports 世界の観戦者数, 今年は 4 億 4,300 万人突破の見通し, (2020 年 1 月 28 日参照 ) [ 2 ] 小中学生の遊びに関する意識調査, バンダイ, pdf,(2020 年 1 月 28 日参照 ) [ 3 ] 遠藤雅伸 ゲームを面白くするコツ第 5 回 ちょうどいい と感じる難易度調整,WEB+DB PRESS, 技術評論社,2017, Vol. 97. [ 4 ] Katsuya Fujii, Jun Rekimoto: SubMe: An Interactive Subtitle System with English Skill Estimation Using Eye Tracking, Proceedings of the 10th Augmented Human International Conference, 2019, 23: 1 23: 9. [ 5 ] 両角貴弘, 大囿忠親, 新谷虎松 弓道練習支援システムにおける射法八節の認識のためのデータ収集について, 2018, 第 32 回人工知能学会全国大会,2F4 04. [ 6 ] 平井重行, 片寄晴弘, 井口征士 歌の調子外れに対する治療支援システム, 電子情報通信学会論文誌,2001, Vol. J84 D II,No.9,pp [ 7 ] 三宅陽一郎 ディジタルゲームにおける人工知能技術の応用, 人工知能学会誌,2008, Vol. 23, No. 1, pp [ 8 ] Vicencio-Moreira, Rodrigo, Mandryk, Regan L., Gutwin, Carl, Bateman, Scott: The ešectiveness (or lack thereof) of aim-assist techniques in ˆrst-person shooter games. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems, ACM, 2014, pp [ 9 ] 榊原諒, 白井暁彦 RTS ゲームのプレイログ分析によるプレイヤー養成システム, エンタテインメントコンピ

5 国士舘大学紀要情報科学第 41 号 (2020 年 ) ューティングシンポジウム論文集,2016, pp [10] 瀧剛志, 長谷川純一 チームスポーツにおける集団行動解析のための特徴量とその応用, 電子情報通信学会論文誌 D, 1998, Vol. 81, No. 8, pp [11] 須俊彦, 高橋正樹, 合志清一, 蓼沼眞, 八木伸行 実時間画像処理に基づくオフサイドライン可視化システム, 電子情報通信学会論文誌 D II, 2005, Vol. 88, No. 8, pp

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