臺灣博碩士論文加值系統

本研究使用感性工學方法，以目前台灣市場中最受歡迎的大型多人線上角色扮演遊戲「魔獸世界(World of Warcraft)」為例，分析遊戲角色造形與美術設計，首先，蒐集遊戲中的怪物角色樣本，依型態分析法對樣本進行造型解構，透過焦點團體法(Focus Group Survey)討論後，挑選關鍵的設計造型，定義每個特徵點，繪製出怪物角色之造形特徵表。第二步驟進行怪物角色設計感性意象調查問卷，透過問卷讓受測者對所有樣本進行評價，問卷資料透過因素分析(Factor Analysis)萃取代表性形容詞的反應尺度，這些代表性形容詞將作為分類標籤，將每個樣本歸類到適合的分類標籤底下，利用支援向量機(Support Vector Machine，SVM)建立分類模型。問卷實驗結果透過因素分析，挑選出五個形容詞，如下：adj11笨重的、adj3噁心的、adj6兇猛的、adj5精悍的、adj9巨大的，這五個形容詞最能代表玩家心裡對怪物樣本的感受。
透過焦點團體法進行討論，並依據怪物角色設計相關文獻與型態分析法，進行怪物樣本的特徵點解構，製做怪物特徵設計系統，並可供遊戲美術人員於設計時的參考。本研究使用高斯核函數與多項式核函數的支援向量機建立情感反應分類模型，並利用混淆矩陣分析預測結果，可以發現，高斯核心函數的平均準確率(93.9%)優於多項式核心函數(71.4%)。

In this study, proposes a Kansei engineering approach to research a case study of the most popular in Taiwan market in the massively multiplayer online role-playing game "World of Warcraft ", to analyze the role of modeling and art design. First, collected samples of the game's monster role. According to type Analysis of samples from deconstruct shape. Through focus groups survey discussion, the selection of key design modeling. The definition of each feature point and draw a monster form features of the role of the table. The second step for a monster character design Kansei survey, through questionnaires to samples of all subjects were evaluated. The questionnaire data through factor analysis (FA) extraction of representative adjectives of the response scales. The representation of adjectives as category labels. For each sample to be classified under the category labels. Using SVM (Support Vector Machine, SVM) classification model established. Questionnaire results of factor analysis. The selection of the five adjectives, as: adj11 heavy, adj3 nausea, adj6 ferocious, adj5 vigorous, adj9 huge. The five adjectives that best represents the player the feeling heart of the monster samples.
Through focus group discussions, and according to the literature monster character design and style analysis. Making a monster feature design system for game art and design staff at the reference. In this study, Gaussian kernel and polynomial kernel function of support vector machine classification model of emotional response, and using the results of confusion matrix analysis and forecasting. We find that the Gaussian core function of the average accuracy rate (93.9%) than polynomial kernel function ( 71.4%).

目　　次
中文摘要........................................................... i
英文摘要.......................................................... ii
誌謝............................................................. iii
目次............................................................. iv
表目錄........................................................... vii
圖目錄...........................................................viii
第一章 緒論....................................................... 1
1.1研究背景與動機.................................................. 1
1.1.1線上遊戲產業發展............................................... 1
1.1.2消費者導向之遊戲美術設計........................................ 5
1.2研究目的........................................................ 6
1.3論文架構.........................................................7
1.4研究範圍與限制.................................................... 9
第二章 文獻探討..................................................... 10
2.1遊戲類型與角色發展................................................ 10
2.1.1遊戲分類與定義..................................................10
2.1.2遊戲開發分工與流程...............................................16
2.2遊戲怪物角色設計..................................................17
2.2.1怪物角色的發展與定義.............................................19
2.2.2遊戲怪物角色設計................................................21
2.3感性工學.........................................................22
2.3.1感性工學的定義..................................................22
2.3.2感性工學之應用範疇...............................................24
2.4幾何造形定義......................................................26
2.5小結.............................................................27
2.6型態解構法與型態分析法.............................................28
2.7語意差異法（Method of Semantic Differential）.....................30
2.8因素分析 (Factor Analysis，FA) ...................................30
2.9支援向量機(Support Vector Machine，SVM) ..........................31
第三章 研究方法.....................................................33
3.1研究架構........................................................33
3.2感性語彙篩選.....................................................36
3.2.1蒐集怪物樣本與情感語彙..........................................36
3.2.2語意差異法問卷調查實驗..........................................40
3.2.3因素分析.......................................................41
3.3樣本造形解構與分析................................................42
3-4建立情感反應分類模型...............................................44
3-4-1 怪物造型特徵編碼...............................................44
3-4-2樣本與分類標籤配對...............................................45
3.4.3建立支援向量機模型...............................................45
3.5專家訪談與建立怪物角色設計方法......................................46
3.5.1專家訪談法.............................................46
3.5.2訪談對象...............................................46
3.5.3確定訪談方向............................................47
3.6怪物設計方法與流程.................................................48
第四章 研究結果......................................................49
4.1語意差異問卷與造形解構結果..........................................49
4.1.1語意差異問卷實驗結果.............................................49
4.2樣本造形解構......................................................50
4.2.1怪物造形元素意象分析.............................................52
4.3因素分析篩選結果..................................................53
4.4情感反應分類模型..................................................54
4.5專家訪談內容整理..................................................57
4.5.1訪談資料整理............................................57
4.5.2小結...................................................58
4.6怪物角色設計方法..................................................60
4.7怪物角色設計系統..................................................64
第五章 結論與後續建議.................................................66
5.1研究結果與討論....................................................66
5.2後續研究發展......................................................68
5.3研究貢獻.........................................................69
參考文獻............................................................71

參考文獻
1.Akihiro ISEKI, 2007. Kenji OZAWA, “Effects of Musical Performer Information on the Kansei of Listeners,” International Conference on Kansei Engineering and Emotion Research.
2.Chih-Chieh Yang, 2010, Support Vector Regression Based Prediction Model of Affective Responses forProduct Form Design.
3.Chih-Chieh Yang and Meng-Dar Shieh, 2009, A General Framework for Kansei Engineering System.
4.Chih-Chieh Yang and Meng-Dar Shieh, 2009, Product Form Feature Selection for Mobile Phone Design using LS-SVR and ARD.
5.Chih-Chieh Yang and Meng-Dar Shieh, 2009, Modeling affective responses for product form design based on consumer segmentation and information fusion.
6.Cawley, G.C. 2004 Talbot, N.L.C., “Fast exact leave-one-out cross-validation of sparse least-squares support vector machines,” Neural Networks, 17, pp.1467-1475.
7.Cristianini, N. & Shawe-Taylor, J. 2000, An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press.
8.Chen, W. H. & Shih, J. Y. 2006, study of Taiwan’s issuer credit rating systems using support vector machines, Expert Systems with Applications, 30, 427-435.
9.Drezet, P.M.L., Harison, R.F. 2002, “A new method for sparsity control in support vector classification and regression,” Pattern Recognition, pp.111-125.
10.DeMaria, R. and Wilson, J. L. 2001,High Score!: The Illustrated History of Electronic Games, McGraw-Hill.
11.Davis, C.(2010). Character Design for Games and Animation Volume 1. Hollywood. CA: The Gnomon Workshop.
12.Davis, C.(2010). Character Design for Games and Animation Volume 2. Hollywood. CA: The Gnomon Workshop.
13.Erik M. Schmidt.2010.Feature selection for content-based, time-varying musical emotion regression.
14.Gillian Smith2009 Rhythm-Based Level Generation for 2D Platformers
Christopher Pedersen 2010 Modeling Player Experience for Content Creation IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES.
15.Hirohiko A. 1999, An Hierarchical Representation of the Consumer Value Structure using Qualitative Data, Report of Modeling the Evaluation Structure of Kansei.
16.Hung-Yuan Chen.2009. Extraction of product form features critical to determining consumers’ perceptions of product image using a numerical definition-based systematic approach. International Journal of Industrial Ergonomics 39 (2009) 133–145.
17.Jeannie Novak，2004，Game Development Essentials.
18.Julian Togelius and Jurgen Schmidhuber 2008 An Experiment in Automatic Game Design.
19.John Chris Jones，1992，Design Method
20.Kirke, B., Madeleine Vionnet 1991, San Francisco: Chronicle Books,.
21.Kalay, Y. E., Principles of Computer-aided Design: Modeling Objects and Environments. New York, NY: John Wiley & Sons, 1989.
22.Kim, J. O. And Mueller, C. W. (1978). Factor Analysis: Statistical Methods and Practical Issues. Newbury Park, Sage Publication.
23.Mitsuo Nagamachi，1993，Merchandise emotional, Tokyo, Japan, first edition
24.Mitsuo Nagamachi，1995，Kansei Engineering, Japan Standards Association, Tokyo, Japan, first edition
25.Meretzky, S., 2001, “Building Character： An Analysis of Character Creation” .
26.Manninen, T., & Kujanpää, T. (2007). The Value of Virtual Assets-The Role of Game Characters in MMOGs. International Journal of Business Science and Applied Management, (2)1, pp. 21-33.
27.Marianne Krawczyk、Jeannie Novak，2004，Game Development Essentials Game Story & Character Development.
28.Nagamachi, M. (1995). Kansei Engineering: a new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15(1), 311-346.
29.Nunnally, J. C. (1967). Psychometric Theory. USA, mcgraw-Hill.
30.Osgood, C. E., The nature and measurement of meaning. Psychol Bull, 49(3),
197-237, 1952.
31.Petri Lankoski，2004，Character Design Fundamentals for Role-Playing Games.
32.Quinlan,1993, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, CA., USA.
33.Riget, J. & Vesterstroem,2002, J. S, A diversity-guided particle swarmoptimizer-the ARPSO, Technical Report No. 2002-02 Department of Computer Science University of Aarhus.
34.Rakotomamonjy, A. ,2003, Variable selection using SVM-based criteria. Journal of Machine Learning Research, 3, 1357-1370.
35.Rollings, A. and Adams, E., 2003, Andrew Rollings and Ernest Adams on Game
Design, New Riders, Indianapolis.
36.Shi, Y. & Eberhart, R. C.1998,A modified particle swarm optimizer, Proceedings of the IEEE Congress on Evolutionary Computation, Piscataway, New Jersey, 69-73.
37.Scholkopf, B., Burges, C. J. C. & Smola, A. J.,1999,Introduction to Support Vector Learning, Advances in Kernel Methods-Support Vector Learning, Cambridge. MA, 1-15.
38.Steve Meretzky November 20, 2001 Building Character: An Analysis of Character Creation Gamasutra, from
http://www.gamasutra.com/resource_guide/20011119/meretzky_01.htm.
39.Scott W. McQuiggan, Sunyoung Lee, and James C. Lester 2006 Predicting User Physiological Response for Interactive Environments: An Inductive Approach
40.Shang Hwa Hsu,2007 Factors Influencing Player Preferences for Heroic Roles in Role-Playing Games.
41.Shang Hwa Hsu,2009, Design facial appearance for roles in video games. Expert Systems with Applications 36 (2009) 4929–4934.
42.Shieh, M. D. & Yang, C. C., Classification model for product form design using fuzzy support vector machines, Computers & Industrial Engineering, 55, 150–164, 2008.
43.Smith S. & Fu S. H., 2011.The relationships between automobile head-up display presentation images and drivers’ Kansei, Displays, 32(2), 58-68.
44.Takerube nobuaki，2008，Encyclopedia of Phantasmata Monster.
45.Tuomas Eerola ,2009 ,Prediction of multidimensional emotional ratings in music from audio using multivariate regression models.
46.Vogler, C., 1998, The Writer’s Journey: Mythic Structure for Writers.
47.Yuichiro KINOSHITA, Sadayuki ICHINOHE, Yoshiaki SAKAKURA, Eric W. COOPER, and Katsuari KAMEI, “Kansei Product Design for the Active Senior Generation- A Case Study of Mobile Phone Designs,” International Conference on Kansei Engineering and Emotion Research ,2007.
48.Yee, N. (2006). Motivations for Play in Online Games. Journal of CyberPsychology and Behavior, 9(6), pp.772-775.
49.Yang, C. C. 2011, Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design, Department of Multimedia and Entertainment Science.
50.Yang, C. C. 2011, A classification-based Kansei engineering system for modeling consumers’ affective responses and analyzing product form features, Expert Systems with Application.
51.Zwicky, F., 1967, The morphological Approach to discovery, invention, research and construction.
52.范立璋， 2008，具網際網路社群回饋支援之電腦輔助遊戲怪物設計參照系統，南台科技大學，多媒體與電腦娛樂科學研究所碩士學位論文。
53.劉以琳，2007，人臉特徵的感性研究：運用於電腦遊戲角色設計。