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系統識別號 U0026-1208201114033900
論文名稱(中文) 以支援向量迴歸方法建立音樂遊戲玩家情感預測模型
論文名稱(英文) An SVR-based Method for Modeling Players' Experience with Application to Musical Game Design
校院名稱 成功大學
系所名稱(中) 工業設計學系碩博士班
系所名稱(英) Department of Industrial Design
學年度 99
學期 2
出版年 100
研究生(中文) 張詠翔
研究生(英文) Yung-Hsiang Chang
學號 p36984104
學位類別 碩士
語文別 中文
論文頁數 75頁
口試委員 指導教授-謝孟達
口試委員-蕭世文
口試委員-楊智傑
中文關鍵字 音樂遊戲  情感遊戲設計  感性工學  支援向量迴歸  心流模型理論 
英文關鍵字 Music game  affect-focused game design  kansei engineering  support vector regression  flow model 
學科別分類
中文摘要   本研究使用支援向量迴歸 ( support vector regression, SVR ) ,針對音樂遊戲玩家的情感建立預測模型。根據心流模型理論 ( flow model ) 遊戲中的主要情感有樂趣 ( Fun ) 、挑戰 ( Challenge ) 、挫折 ( Frustration ) 、可預測性 ( Predictability ) 、焦慮 ( Anxiety ) 、無聊 ( Boredom ) 六個情感因素,遊戲設計師可以進行情感遊戲的設計。而本研究將使用感性工學的理論,先將遊戲特徵進行參數化並製作成遊戲樣本後,結合遊戲中會出現之六個情感因素做成情感問卷調查,再利用支援向量迴歸建立預測模型,毎一組訓練樣本將遊戲的參數作為輸入值,問卷所得的情感分數為輸出值。遊戲設計師可以利用本研究提出的情感預測模型來掌握玩家的情感,作為設計遊戲時的依據。
英文摘要 This study uses support vector regression (SVR) and constructs a predictive model aimed at music game player. According to flow model theory, emotions in games mainly include six affective factors: Fun, Challenge, Frustration, Predictability, Anxiety and Boredom. Game designers can design emotion game according to flow theory. Using theories of Kansei Engineering, this study first parameterizes game features, produces game samples, and makes emotion questionnaire survey in combination with six affective factors appearing in the game. Then, SVR is used to establish predictive model; each group of training sample adopts game parameters as input and emotion questionnaire scores as output. Game designers can utilize the predictive model for emotion proposed in this research to grasp players’ mood and feelings, and consider this model as a basis of game design.
論文目次 Abstract II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1-1 研究背景 1
1-2 電玩遊戲 3
1-3 研究目的 4
1-4 研究範圍與限制 5
1-5 研究流程 5
第二章 文獻探討 7
2-1 音樂遊戲介紹 7
2-1-1 聽覺、視覺與遊戲 8
2-1-2 音樂遊戲的特徵 8
2-2 以玩家情感為導向之遊戲設計 11
2-3 參數化遊戲設計 13
2-4 自動化遊戲設計 14
2-5 非線性預測模型 15
第三章 研究理論 17
3-1 因素分析 17
3-2 主成分分析法 18
3-3 建立情感預測模型 19
第四章 研究步驟 22
4-1 研究架構 22
4-2 音樂遊戲型態分析及參數化 22
4-3 準備遊戲樣本 27
4-4 遊戲參數化 28
4-5 確認玩家遊戲經驗 34
4-6 遊戲情感問卷 35
4-6-1 問卷設計 36
4-6-2 情感遊戲問卷進行 36
4-7 支援向量迴歸建立預測模型 38
4-7-1 情感問卷結果 39
4-7-2 參數挑選 45
4-7-3 使用支援向量迴歸建立預測模型 48
4-8 實驗結果驗證 53
第五章 結果與討論 55
5-1 實驗結果分析與討論 55
5-2 研究總結 59
5-3 研究後續建議 60
參考文獻 62
附錄一 音樂情感網路問卷 66
附錄二 遊戲情感問卷受測者基本資料與注意事項問卷 67
附錄三 遊戲情感問卷數據資料 69
附錄四 太鼓之達人遊戲說明 75
參考文獻 中文文獻
東方消費者行銷資料庫 E-ICP-活動嗜好與興趣. (2005-2010).
邱皓政. (2005). 量化研究與統計分析:SPSS 中文視窗版資料分析範例解析: 五南.

英文文獻
Chen, J. (2007). Flow in games (and everything else). Commun. ACM, 50(4), 31-34. doi: http://doi.acm.org/10.1145/1232743.1232769
Compton, K., & Mateas, M. (2006). Procedural level design for platform games. Proceedings of the American Association for Artificial Intelligence Conference, 109-111.
Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. New York: Harper and Row.
Hudlicka, E. (2008). Affective computing for game design. Proceedings of the 4th International North American Conference on Intelligent Games and Simulation, 5-12.
Hudlicka, E. (2009). Affective game engines: motivation and requirements. Proceedings of the 4th International Conference on Foundations of Digital Games, 299-306.
Ip, B., & Jacobs, G. (2004). Quantifying game design. [doi: DOI: 10.1016/j.destud.2004.02.001]. Design Studies, 25(6), 607-624.
King, D., Delfabbro, P., & Griffiths, M. (2010). Video Game Structural Characteristics: A New Psychological Taxonomy. International Journal of Mental Health and Addiction, v8, 16.
Liu, C., Agrawal, P., Sarkar, N., & Chen, S. (2009). Dynamic difficulty adjustment in computer games through real-time anxiety-based affective feedback. International Journal of Human-Computer Interaction, 25(6), 506 - 529.
Mandryk, R. L., & Atkins, M. S. (2007). A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. [doi: DOI: 10.1016/j.ijhcs.2006.11.011]. International Journal of Human-Computer Studies, 65(4), 329-347.
Matsubara, Y., & Nagamachi, M. (1997). Hybrid Kansei engineering system and design support. International Journal of Industrial Ergonomics, 19(2), 81-92. doi: Doi: 10.1016/s0169-8141(96)00005-4
Pedersen, C., Togelius, J., & Yannakakis, G. N. (2010). Modeling Player Experience for Content Creation. Computational Intelligence and AI in Games, IEEE Transactions on, 2(1), 54-67.
Penelope, S., & Peta, W. (2005). GameFlow: A model for evaluating player enjoyment in games. Computer in Entertainment ,36 ,170-182.
PricewaterhouseCoopers. (2010). The Global Entertainment & Media Outlook: 2009~2013, 10th annualed, PricewaterhouseCoopers, New York, 2009, 11.
Qin, H., Rau, P. L. P., & Salvendy, G. (2010). Effects of different scenarios of game difficulty on player immersion. Interacting with Computers, 22, 230-239.
Smith, G., Cha, M., & Whitehead, J. (2008). A framework for analysis of 2D platformer levels. Proceedings of the 2008 ACM SIGGRAPH symposium on Video games, 75-80.
Smith, G., Treanor, M., Whitehead, J., & Mateas, M. (2009). Rhythm-based level generation for 2D platformers. Proceedings of the 4th International Conference on Foundations of Digital Games, 175-182. doi: 10.1145/1536513.1536548
Sorenson, N., & Pasquier, P. (2010). The evolution of fun: automatic level design through challenge modeling. Proceedings of the First International Conference on Computational Creativity, 258-267.
Togelius, J., & Schmidhuber, J. (2008). An experiment in automatic game design. Proceedings of the IEEE Symposium on Computational. Intelligence and Games, 111-118.
Wang, W., Xu, Z., Lu, W., & Zhang, X. (2003). Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing, 55, 643-663.
Wolfson, S., & Case, G. (2000). The effects of sound and colour on responses to a computer game. Interacting with Computers, 13(2), 183-192.
Yang, C.C. (2011). Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design. Computers & Industrial Engineering, 60(4), 760-768. doi: DOI: 10.1016/j.cie.2011.01.011
Yang, C.C., & Shieh, M. D. (2010). A support vector regression based prediction model of affective responses for product form design. Computers & Industrial Engineering, 59(4), 682-689. doi: DOI: 10.1016/j.cie.2010.07.019
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