參考文獻 |
【中文文獻】
(1) 古廸耀 (2016)。自動化優化遊戲設計者期待的玩家體驗(碩士論文)。取自https://hdl.handle.net/11296/x69m8p
(2) 陳彥翰 (2012)。吃角子老虎機中維持穩定中獎率與回饋率之研究(碩士論文)。取自 https://hdl.handle.net/11296/eb8498
(3) 袁梅宇,王者歸來WEKA機器學習與大數據聖經,第三版,台北市,佳魁資訊,2017。
(4) 張信宏 (2016)。老虎機遊戲之自動中獎表產生器研究(碩士論文)。取自https://hdl.handle.net/11296/xp65f8
(5) 曾國書 (2017)。博弈娛樂場顧客行為特徵與動機相關性之研究-以新竹地區為例(碩士論文)。取自https://hdl.handle.net/11296/m3cfrv
(6) 遊戲宅宅 (2018年10月8日)。老虎機的遊戲操作與玩法規則[部落格文字資料]。取自https://ezslotdesign.com/slotgame_ui/,存取時間:2020年 1月22日。
(7) 電子遊戲場業管理條例 (2016年12月28日) 。
(8) 維基百科 (2020),「老虎機」,https://zh.wikipedia.org/wiki/%E8%A7%92%E5%AD%90%E6%A9%9F,存取時間:2020年3月31日。
(9) 澳門特別行政區政府博彩監察協調局 (2020),「老虎機技術標準第1.1版」, http://www.dicj.gov.mo/web/files/standards/Macau_EGM_Technical_Standards_v1.1-CN.pdf,存取時間:2020年12月9日。
(10) 謝邦昌,資料採礦入門及應用 ─ 從統計技術看資料採礦,台北市,資商訊息股份有限公司,2013。
(11) 謝邦昌,Excel 2013 資料採礦完全手冊,台北,元華文創,2017。
【英文文獻】
(1) Agrawal, R., Imieliński, T., & Swami, A. (1993). “Mining Association Rules between Sets of Items in Large Databases.” In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.
(2) Blaszczynski, A. (2000). “Pathways to Pathological Gambling: Identifying Typologies.” Journal of Gambling, 1. doi: 10.4309/jgi.2000.1.1
(3) Breen, R. B., & Frank, M.L. (1993). “The Effects of Statistical Fluctuations and Perceived Status of a Competitor on the Illusion of Control in Experienced Gamblers.” Journal of Gambling Studies, 9, 265–276.
(4) Coates, E., & Blaszczynski, A. (2013). “Predictors of Return Rate Discrimination in Slot Machine Play.” Journal of Gambling Behavior, 30(3), 669-683.
(5) Currie, S. R., Hodgins, D. C., Wang, J., el-Guebaly, N., Wynne, H., & Chen, S. (2006). “Risk of Harm Among Gamblers in the General Population as a Function of Level of Participation in Gambling Activities.” Addiction, 101(4), 570–580.
(6) Dixon, M. J., Graydon, C., Harrigan, K. A., Wojtowicz, L., Siu, V., & Fugelsang, J. A. (2014). “The Allure of Multi‐Line Games in Modern Slot Machines.” Addiction, 109(11), 1920‐1928.
(7) Dixon, M. R., Maclin, O. H. & Daugherty, D. (2006). “An Evaluation of Response Allocations to Concurrently Available Slot Machine Simulations.” Behavior Research Methods, 38, 232–236.
(8) Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). “The KDD Process for Extracting Useful Knowledge from Volumes of Data.” Communications of the ACM, 39(11), 27-34.
(9) Griffiths, M. (1993). “Fruit Machine Gambling: The Importance of Structural Characteristics.” Journal of Gambling Studies, 9, 101–120.
(10) Griffiths, M. (1999). “Gambling Technologies: Prospects for Problem Gambling.” Journal of Gambling Studies, 15, 265-283.
(11) Han, J., Kamber, M., & Pei, J. (2011). “Data Mining: Concepts and Techniques.” San Francisco: Morgan Kaufmann Publishers Inc.
(12) Harrigan, K.A. & Dixon, M. (2009). “PAR Sheets, Probabilities, and Slot Machine Play: Implications for Problem and Non-Problem Gambling.” Journal of Gambling Issues, 23, 81-110.
(13) Harris, A., & Griffiths, M.D. (2018). “The Impact of Speed of Play in Gambling on Psychological and Behavioural Factors: a Critical Review.” Journal of Gambling Studies, 34, 393–412.
(14) IGT (2020). “Systems.” (accessed 2020/03/31, available at: https://www.igt.com/products-and-services/gaming/systems).
(15) Inmon, W. H. & Hackathorn, R. D. (1994). “Using the Data Warehouse.” New York, NY: John Wiley & Sons.
(16) Langer, E. J., & Roth, J. (1975). “Heads I Win, Tails It′s Chance: The Illusion of Control as a Function of the Sequence of Outcomes in a Purely Chance Task.” Journal of Personality and Social Psychology, 32(6), 951–955.
(17) Livingstone, C., Woolley, R., Zazryn, T., Bakacs, L., & Shami, R. (2008). “The Relevance and Role of Gaming Machine Games and Game Features on the Play of Problem Gamblers.” Adelaide, South Australia: Independent Gambling Authority of South Australia.
(18) Lucas, A. F., & Brandmeir, K. D. (2005). “Estimatin the Short-Term Effects of an Increase in Par on Reel Slot Performance.” UNLV Gaming Research & Review Journal, 9(2), 1-14.
(19) Lucas, A. F., & Singh, A. K. (2011). “Estimating the Ability of Gamblers to Detect Differences in the Payback Percentages of Reel Slot Machines: A Closer Look at the Slot Player Experience.” UNLV Gaming Research & Review Journal, 15, 17-36.
(20) Lucas, A. F., Singh, A. K., & Gewali, L. (2007). “Simulating the Effect of Pay Table Standard Deviation on Pulls Per Losing Player at the Single-Visit Level.” UNLV Gaming Research & Review Journal, 11(1), 41-52.
(21) Nisbet, S. (2005). “Responsible Gambling Features of Card-Based Technologies.” International Journal of Mental Health and Addiction, 3(2), 54-63.
(22) Pachman, D. (2018, April 5). Re: Understanding a Slot Players Volatility Profile. [Web blog message]. Retrieved from https://linkedin.com/pulse/understanding-slot-players-volatility-profile-darrin-pachman/
(23) Thomas A., Delfabbro, P., & Armstrong, A. (2014). “Validation Study of In-Venue Problem Gambling Indicators.” Melbourne: Gambling Research Australia.
(24) Weir, J. (1998). “Data Mining: Exploring the Corporate Asset.” Information Systems Management, 15(4), 68-71.
(25) WEKA (2020). “WEKA.” (accessed 2020/03/02, available at: https://www.cs.waikato.ac.nz/ml/weka/). |