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題名 多人線上戰鬥競技場遊戲之團隊成員推薦機制
A Team Member Recommender System for Multiplayer Online Battle Arenas作者 周佩諄
Chou, Pei Chun貢獻者 沈錳坤
Shan, Man Kwan
周佩諄
Chou, Pei Chun關鍵詞 多人線上戰鬥競技場遊戲
線上遊戲
團隊成員推薦
績效預測
遊戲資料探勘
MOBA
Online game
Team member recommendation
Outcome prediction
Game data mining日期 2018 上傳時間 2-Mar-2018 11:49:41 (UTC+8) 摘要 近幾年來遊戲軟硬體的進步以及遊玩人數的增加，虛擬世界中的使用者行為已經開始受到注目，也有研究指出人們在虛擬世界的行為會反應他們在現實世界的行為並且交互影響。現今最熱門的線上遊戲更是提供多樣化的機制讓玩家們進行合作、競爭、交流等活動，遊戲開發者也會根據不同的目的開始分析玩家的行為，希望能藉此發現遊戲中更多的可能性。遊戲的種類繁多，遊玩機制也相當多元，目前是以MOBA這類的線上遊戲最為熱門、擁有最多的玩家基數，MOBA是基於團隊合作的對戰型遊戲，玩家可以自由選擇多種職業（或稱作角色）的其中一種並和其他4位玩家組成隊伍，而對手也是同樣由5位玩家組成的隊伍。這類遊戲最大特色是職業的組合關係以及玩家之間的合作關係。在各個遊戲論壇或電競場合中，玩家們對於找出最佳的團隊組成或遊戲技巧提高勝率的分析相當熱衷，但在學術研究領域上目前針對線上遊戲團隊還沒有太多深入的研究。本研究的目標旨在提出一個結合資料探勘與社群網路分析的方法來分析玩家與團隊績效之間的關係，並用於團隊績效預測與團隊組成上，藉此進行隊友的推薦。首先從抓取來的資料中取出三種玩家與英雄之間的關係，考量玩家的合作關係與英雄的組合關係，藉此篩選出具有高相關度的玩家作為推薦候選人。而在團隊績效預測的部分，取出對玩家個人表現或團隊表現具有影響的特徵值，並分析勝利的玩家或團隊通常會具備什麼樣的特質，再進行團隊表現的預測模型的建置。最後再結合兩者推薦出適合此隊伍的隊友供團隊選擇。
Multiplayer online battle arenas (MOBA) is a subgenre of strategy games and has become the most popular online game genres recently. Teams of players could fight against each other in arena environments. To find good team members when playing MOBA is a challenge. In this thesis, we proposed a team member recommender mechanism to recommend team members for MOBA. The proposed mechanism first takes the team chemistry into consideration and generates the candidates based on the cooperation history among players and associated heroes. Then the proposed win/lose prediction model is employed to predict the win rate of each candidate by considering characteristics and proficiency of players and associated heroes. The recommended team members are ranked according to the predicted win rates. The experiments show that the proposed win/lose prediction model achieves approximately 91.6% accuracy and our mechanism could recommend players who have close cooperation with query players instead of considering the win rate only. Our proposed method could help the team formation and may enhance team performance of the on-line game.參考文獻 [1] A. Canossa, J. B. Martinez, and J. Togelius, Give me a reason to dig Minecraft and psychology of motivation. IEEE Conference on Computational Intelligence in Games, 2013.[2] H. Cole and M. D. Griffiths, Social interactions in massively multiplayer online role-playing gamers. CyberPsychology & Behavior, 10(4), 575-583, 2007.[3] K. Conley and D. Perry, How does he saw me? A recommendation engine for picking heroes in Dota 2. Project Report, CS229 Maching Learning Course, Stanford University, 2013.[4] A. Drachen and A. Canossa, Evaluating motion: Spatial user behaviour in virtual environments. International Journal of Arts and Technology, 4(3), 294-314, 2011.[5] A. Drachen, R. Sifa, C. Bauckhage, and C. Thurau, Guns, swords and data: Clustering of player behavior in computer games in the wild. IEEE Conference on Computational Intelligence and Games, 2012.[6] A. Drachen, C. Thurau, R. Sifa, and C. Bauckhage, A comparison of methods for player clustering via behavioral telemetry. International Conference on the Foundations of Digital Games, 2013.[7] A. Drachen, M. Yancey, J. Maguire, D. Chu, I. Y. Wang, T. Mahlmann, M. Schubert, and D. Klabajan, Skill-based differences in spatio-temporal team behaviour in defence of the ancients 2 (dota 2). IEEE Conference on Games media entertainment, 2014.[8] Z. Fang, X. Zhou, J. Tang, W. Shao, A. C. M. Fong, L. Sun, Y. Ding, L. Zhou, and J. Luo, Modeling paying behavior in game social networks. ACM Conference on Information and Knowledge Management, 2014.[9] L. Guo, J. Shao, K. L. Tan, and Y. Yang, Wheretogo: Personalized travel recommendation for individuals and groups. International Conference on Mobile Data Management, 2014.[10] F. Johansson and J. Wikström, Result prediction by mining replays in Dota 2. Master Thesis, Department of Computer Science and Engineering, Faculty of Computing, Blekinge Institute of Technology, 2015[11] S. Köhler, S. Bauer, D. Horn, and P. N. Robinson, Walking the interactome for prioritization of candidate disease genes. The American Journal of Human Genetics, 82(4), 949-958, 2008.[12] J. H. Kim, D. V. Gunn, E. Schuh, B. Phillips, R. J. Pagulayan, and D. Wixon, Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems. SIGCHI conference on Human Factors in Computing Systems, 2008.[13] S. Müller, S. Frey, M. Kapadia, S. Klinger, R. P. Mann, B. Solenthaler, R. W. Sumner, and M. Gross, HEAPCRAFT: Quantifying and predicting collaboration in Minecraft. AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2015.[14] S. Müller, M. Kapadia, S. Frey, S. Klinger, R. P. Mann, B. Solenthaler, R. W. Sumner, and M. Gross, Statistical analysis of player behavior in Minecraft. IEEE Conference on the Foundations of Digital Games, 2015.[15] T. Mahlmann, A. Drachen, J. Togelius, A. Canossa, and G. N. Yannakakis, Predicting player behavior in Tomb raider: Underworld. IEEE Symposium on Computational Intelligence and Games, 2010.[16] D. Moura, M. S. El-Nasr, and C. D. Shaw, Visualizing and understanding players` behavior in video games: discovering patterns and supporting aggregation and comparison. ACM SIGGRAPH 2011 Game Papers, 2011.[17] T. Nuangjumnonga and H. Mitomo, Leadership development through online gaming. 19th ITS Biennial Conference, Bangkok 2012: Moving Forward with Future Technologies-Opening a Platform for All, 2012.[18] H. Y. Ong, S. Deolalikar, and M. Peng, Player behavior and optimal team composition for online multiplayer games. Project Report, CS229 Maching Learning Course, Stanford University, 2015.[19] N. Pobiedina, J. Neidhardt, M. D. C. Calatrava Moreno, and H. Werthner, Ranking factors of team success. International Conference on World Wide Web, 2013.[20] F. Rioult, J.-P. Métivier, B. Helleu, N. Scelles, and C. Durand, Mining tracks of competitive video games. AASRI Procedia, 8, 82-87, 2014.[21] A. Semenov, P. Romov, S. Korolev, D. Yashkov, and K. Neklyudov, Performance of machine learning algorithms in predicting game outcome from drafts in Dota 2. International Conference on Analysis of Images, Social Networks and Texts, 2016.[22] K. J. Shim and J. Srivastava, Team performance prediction in massively multiplayer online role-playing games (MMORPGs). IEEE Conference on Social Computing, 2010.[23] R. Sifaa, A. Drachen, and C. Bauckhage, Profiling in games: Understanding behavior from telemetry. In Social Interaction in Virtual Worlds, Cambridge University Press, 2017.[24] K. Song, T. Zhang, and C. Ma, Predicting the winning side of DotA2. Project Report ,CS229 Maching Learning Course, Stanford University, 2015.[25] H. Tong, C. Faloutsos, and J.-Y. Pan, Fast random walk with restart and its applications. IEEE International Conference on Data Mining, 2006.[26] H. Wang, B. Xia, and Z. Chen, Cultural difference on team performance between chinese and americans in multiplayer online battle arena games. International Conference on Cross-Cultural Design, 2015.[27] B. G. Weber and M. Mateas, A data mining approach to strategy prediction. IEEE Symposium on Computational Intelligence and Games, 2009.[28] P. Yang, B. E. Harrison, and D. L. Roberts, Identifying patterns in combat that are predictive of success in MOBA games. International Conference on the Foundations of Digital Games, 2014.[29] N. Yee, Motivations for play in online games. CyberPsychology & Behavior, 9(6), 772-775, 2006.[30] 邱楚翔, 《團隊表現績效預測：以NBA籃球運動為例》, 國立政治大學資訊科學系碩士論文, 2014. 描述 碩士
國立政治大學
資訊科學學系
104753041資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104753041 資料類型 thesis dc.contributor.advisor 沈錳坤 zh_TW dc.contributor.advisor Shan, Man Kwan en_US dc.contributor.author (Authors) 周佩諄 zh_TW dc.contributor.author (Authors) Chou, Pei Chun en_US dc.creator (作者) 周佩諄 zh_TW dc.creator (作者) Chou, Pei Chun en_US dc.date (日期) 2018 en_US dc.date.accessioned 2-Mar-2018 11:49:41 (UTC+8) - dc.date.available 2-Mar-2018 11:49:41 (UTC+8) - dc.date.issued (上傳時間) 2-Mar-2018 11:49:41 (UTC+8) - dc.identifier (Other Identifiers) G0104753041 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/116080 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 104753041 zh_TW dc.description.abstract (摘要) 近幾年來遊戲軟硬體的進步以及遊玩人數的增加，虛擬世界中的使用者行為已經開始受到注目，也有研究指出人們在虛擬世界的行為會反應他們在現實世界的行為並且交互影響。現今最熱門的線上遊戲更是提供多樣化的機制讓玩家們進行合作、競爭、交流等活動，遊戲開發者也會根據不同的目的開始分析玩家的行為，希望能藉此發現遊戲中更多的可能性。遊戲的種類繁多，遊玩機制也相當多元，目前是以MOBA這類的線上遊戲最為熱門、擁有最多的玩家基數，MOBA是基於團隊合作的對戰型遊戲，玩家可以自由選擇多種職業（或稱作角色）的其中一種並和其他4位玩家組成隊伍，而對手也是同樣由5位玩家組成的隊伍。這類遊戲最大特色是職業的組合關係以及玩家之間的合作關係。在各個遊戲論壇或電競場合中，玩家們對於找出最佳的團隊組成或遊戲技巧提高勝率的分析相當熱衷，但在學術研究領域上目前針對線上遊戲團隊還沒有太多深入的研究。本研究的目標旨在提出一個結合資料探勘與社群網路分析的方法來分析玩家與團隊績效之間的關係，並用於團隊績效預測與團隊組成上，藉此進行隊友的推薦。首先從抓取來的資料中取出三種玩家與英雄之間的關係，考量玩家的合作關係與英雄的組合關係，藉此篩選出具有高相關度的玩家作為推薦候選人。而在團隊績效預測的部分，取出對玩家個人表現或團隊表現具有影響的特徵值，並分析勝利的玩家或團隊通常會具備什麼樣的特質，再進行團隊表現的預測模型的建置。最後再結合兩者推薦出適合此隊伍的隊友供團隊選擇。 zh_TW dc.description.abstract (摘要) Multiplayer online battle arenas (MOBA) is a subgenre of strategy games and has become the most popular online game genres recently. Teams of players could fight against each other in arena environments. To find good team members when playing MOBA is a challenge. In this thesis, we proposed a team member recommender mechanism to recommend team members for MOBA. The proposed mechanism first takes the team chemistry into consideration and generates the candidates based on the cooperation history among players and associated heroes. Then the proposed win/lose prediction model is employed to predict the win rate of each candidate by considering characteristics and proficiency of players and associated heroes. The recommended team members are ranked according to the predicted win rates. The experiments show that the proposed win/lose prediction model achieves approximately 91.6% accuracy and our mechanism could recommend players who have close cooperation with query players instead of considering the win rate only. Our proposed method could help the team formation and may enhance team performance of the on-line game. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究動機與目的 31.3 論文架構 4第二章 相關研究 52.1 線上遊戲 52.1.1 線上遊戲簡介 52.1.2 多人線上戰鬥競技場遊戲 52.2 線上遊戲資料分析 62.2.1 玩家行為模式探討與預測分析 62.2.2 資料視覺化工具設計與分析 82.2.3 單人與團隊比較分析 92.2.4 對戰遊戲團隊績效預測 102.2.5 本研究與相關研究之差異 11第三章 研究方法 123.1 研究架構 123.2 DOTA 2 133.3 系統架構（System Architecture） 153.4 資料蒐集（Data Collection） 173.5 特徵值擷取（Feature Extraction） 183.6 隊友候選人篩選（Team Member Candidates Generation） 243.7 勝負預測模型與隊友推薦（Win/Lose Prediction & Score Ranking） 31第四章 實驗與成果 334.1 系統實作環境與工具 334.2 資料蒐集與篩選 344.3 不同分類演算法的團隊績效預測比較 344.4 隊友推薦評估 37第五章 結論與討論 415.1 結論與未來發展 41參考文獻 42 zh_TW dc.format.extent 1860854 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104753041 en_US dc.subject (關鍵詞) 多人線上戰鬥競技場遊戲 zh_TW dc.subject (關鍵詞) 線上遊戲 zh_TW dc.subject (關鍵詞) 團隊成員推薦 zh_TW dc.subject (關鍵詞) 績效預測 zh_TW dc.subject (關鍵詞) 遊戲資料探勘 zh_TW dc.subject (關鍵詞) MOBA en_US dc.subject (關鍵詞) Online game en_US dc.subject (關鍵詞) Team member recommendation en_US dc.subject (關鍵詞) Outcome prediction en_US dc.subject (關鍵詞) Game data mining en_US dc.title (題名) 多人線上戰鬥競技場遊戲之團隊成員推薦機制 zh_TW dc.title (題名) A Team Member Recommender System for Multiplayer Online Battle Arenas en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] A. Canossa, J. B. Martinez, and J. Togelius, Give me a reason to dig Minecraft and psychology of motivation. IEEE Conference on Computational Intelligence in Games, 2013.[2] H. Cole and M. D. Griffiths, Social interactions in massively multiplayer online role-playing gamers. CyberPsychology & Behavior, 10(4), 575-583, 2007.[3] K. Conley and D. Perry, How does he saw me? A recommendation engine for picking heroes in Dota 2. Project Report, CS229 Maching Learning Course, Stanford University, 2013.[4] A. Drachen and A. Canossa, Evaluating motion: Spatial user behaviour in virtual environments. International Journal of Arts and Technology, 4(3), 294-314, 2011.[5] A. Drachen, R. Sifa, C. Bauckhage, and C. Thurau, Guns, swords and data: Clustering of player behavior in computer games in the wild. IEEE Conference on Computational Intelligence and Games, 2012.[6] A. Drachen, C. Thurau, R. Sifa, and C. Bauckhage, A comparison of methods for player clustering via behavioral telemetry. International Conference on the Foundations of Digital Games, 2013.[7] A. Drachen, M. Yancey, J. Maguire, D. Chu, I. Y. Wang, T. Mahlmann, M. Schubert, and D. Klabajan, Skill-based differences in spatio-temporal team behaviour in defence of the ancients 2 (dota 2). IEEE Conference on Games media entertainment, 2014.[8] Z. Fang, X. Zhou, J. Tang, W. Shao, A. C. M. Fong, L. Sun, Y. Ding, L. Zhou, and J. Luo, Modeling paying behavior in game social networks. ACM Conference on Information and Knowledge Management, 2014.[9] L. Guo, J. Shao, K. L. Tan, and Y. Yang, Wheretogo: Personalized travel recommendation for individuals and groups. International Conference on Mobile Data Management, 2014.[10] F. Johansson and J. Wikström, Result prediction by mining replays in Dota 2. Master Thesis, Department of Computer Science and Engineering, Faculty of Computing, Blekinge Institute of Technology, 2015[11] S. Köhler, S. Bauer, D. Horn, and P. N. Robinson, Walking the interactome for prioritization of candidate disease genes. The American Journal of Human Genetics, 82(4), 949-958, 2008.[12] J. H. Kim, D. V. Gunn, E. Schuh, B. Phillips, R. J. Pagulayan, and D. Wixon, Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems. SIGCHI conference on Human Factors in Computing Systems, 2008.[13] S. Müller, S. Frey, M. Kapadia, S. Klinger, R. P. Mann, B. Solenthaler, R. W. Sumner, and M. Gross, HEAPCRAFT: Quantifying and predicting collaboration in Minecraft. AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2015.[14] S. Müller, M. Kapadia, S. Frey, S. Klinger, R. P. Mann, B. Solenthaler, R. W. Sumner, and M. Gross, Statistical analysis of player behavior in Minecraft. IEEE Conference on the Foundations of Digital Games, 2015.[15] T. Mahlmann, A. Drachen, J. Togelius, A. Canossa, and G. N. Yannakakis, Predicting player behavior in Tomb raider: Underworld. IEEE Symposium on Computational Intelligence and Games, 2010.[16] D. Moura, M. S. El-Nasr, and C. D. Shaw, Visualizing and understanding players` behavior in video games: discovering patterns and supporting aggregation and comparison. ACM SIGGRAPH 2011 Game Papers, 2011.[17] T. Nuangjumnonga and H. Mitomo, Leadership development through online gaming. 19th ITS Biennial Conference, Bangkok 2012: Moving Forward with Future Technologies-Opening a Platform for All, 2012.[18] H. Y. Ong, S. Deolalikar, and M. Peng, Player behavior and optimal team composition for online multiplayer games. Project Report, CS229 Maching Learning Course, Stanford University, 2015.[19] N. Pobiedina, J. Neidhardt, M. D. C. Calatrava Moreno, and H. Werthner, Ranking factors of team success. International Conference on World Wide Web, 2013.[20] F. Rioult, J.-P. Métivier, B. Helleu, N. Scelles, and C. Durand, Mining tracks of competitive video games. AASRI Procedia, 8, 82-87, 2014.[21] A. Semenov, P. Romov, S. Korolev, D. Yashkov, and K. Neklyudov, Performance of machine learning algorithms in predicting game outcome from drafts in Dota 2. International Conference on Analysis of Images, Social Networks and Texts, 2016.[22] K. J. Shim and J. Srivastava, Team performance prediction in massively multiplayer online role-playing games (MMORPGs). IEEE Conference on Social Computing, 2010.[23] R. Sifaa, A. Drachen, and C. Bauckhage, Profiling in games: Understanding behavior from telemetry. In Social Interaction in Virtual Worlds, Cambridge University Press, 2017.[24] K. Song, T. Zhang, and C. Ma, Predicting the winning side of DotA2. Project Report ,CS229 Maching Learning Course, Stanford University, 2015.[25] H. Tong, C. Faloutsos, and J.-Y. Pan, Fast random walk with restart and its applications. IEEE International Conference on Data Mining, 2006.[26] H. Wang, B. Xia, and Z. Chen, Cultural difference on team performance between chinese and americans in multiplayer online battle arena games. International Conference on Cross-Cultural Design, 2015.[27] B. G. Weber and M. Mateas, A data mining approach to strategy prediction. IEEE Symposium on Computational Intelligence and Games, 2009.[28] P. Yang, B. E. Harrison, and D. L. Roberts, Identifying patterns in combat that are predictive of success in MOBA games. International Conference on the Foundations of Digital Games, 2014.[29] N. Yee, Motivations for play in online games. CyberPsychology & Behavior, 9(6), 772-775, 2006.[30] 邱楚翔, 《團隊表現績效預測：以NBA籃球運動為例》, 國立政治大學資訊科學系碩士論文, 2014. zh_TW
