本研究旨在探索遊戲體驗的相關因素以及提升玩家的遊戲體驗。我們運用遊戲改良理論和心流理論的概念，針對大老二這款撲克牌遊戲進行研究。

首先，我們進行了遊戲分析，深入了解遊戲中可能存在的問題和不足之處，這些問題包括遊戲過於依賴運氣成分，可能導致玩家在遊戲初期就認為勝負已定，此外，我們也注意到大老二的 GR 值相對較高，表明遊戲偏向娛樂，這可能會導致玩家的勝負欲與參與感之間的不平衡，為了改進這些問題，我們提出了調整遊戲的策略，減少純粹的運氣因素，提高玩家的技能和策略的影響力，並調整 GR 值以實現遊戲難度的理想平衡；接著，我們設計了玩家實力評估方法，考慮了勝率、得分等 KPI 值，以確定玩家的實力水平。此外，我們還優化了發牌策略，利用同步模擬的方式縮短篩選牌局的時間；最後，將玩家體驗相關的 KPI 值納入考量，以提升玩家的遊戲體驗。

我們進行了模擬實驗並收集了數據，通過統計分析和檢定，證明了我們的方法的有效性和可行性，實驗結果顯示，高手玩家在我們改進過後的系統會獲得更佳的遊戲體驗，並且調整發牌的方面符合隨機分布。最後，我們提出了結論並討論了未來的研究方向，希望這些研究成果對於改善遊戲體驗和提升玩家滿意度有所貢獻。

This study aims to explore the factors related to game experience and enhance players' gaming experience. We applied the concepts of game refinement theory and flow theoryto examine the popular poker game "Big Two".

First, we conducted a game analysis to gain a deeper understanding of potential issues and shortcomings within the game. These issues included excessive reliance on luck, which might lead players to feel that the outcome is predetermined early in the game. Additionally, we noticed that the game's Game Refinement value(GR) was relatively high, indicating a focus on entertainment, potentially causing an imbalance between players' desire for victory and their sense of participation. To address these problems, we proposed adjustments to the game’s strategies to reduce pure luck factors, increase the influence of player skill and strategy, and fine-tune the GR value to achieve an ideal balance of difficulty.

Next, we designed a player skill assessment method, considering key performance indicators (KPIs) such as win rates and scores to determine a player's skill level. We also optimized the card dealing strategy, shortening the time for screening game rounds using synchronous simulations. Finally, we incorporated player experience-related KPIs into our considerations to enhance the overall gaming experience.

We conducted simulation experiments and collected data, and through statistical analysis and testing, we demonstrated the effectiveness and feasibility of our methods.The results indicated that expert players had a better gaming experience with our improved system, and the adjustments to card dealing followed a random distribution. In conclusion, we discussed future research directions and hope that these findings contribute to improving the gaming experience and increasing player satisfaction.

[1] C.-F. Shi and W.-K. Tai, “A study on gameplay guaranteed game AI: A case study on big two card game.” Unpublished master’s thesis, 2022.
[2] Sugiyanto, G. Fernando, and W.-K. Tai, “A rule-based AI method for an agent playing big two,” Applied Sciences, vol. 11, no. 9, 2021.
[3] L. F. Teófilo, L. P. Reis, and H. L. Cardoso, “A profitable online no-limit poker playing agent,” in 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 286–293, IEEE, 2014.
[4] R. D. S. Canizales, “Implementation of a pre-assessment module to improve the initial player experience using previous gaming information,” 2023.
[5] H. Iida, K. Takahara, J. Nagashima, Y. Kajihara, and T. Hashimoto, “An application of game-refinement theory to mah jong,” ICEC, vol. 3166, pp. 333–338, 2004.
[6] M. N. A. Khalid and H. Iida, “Objectivity and subjectivity in games: understanding engagement and addiction mechanism,” IEEE Access, vol. 9, pp. 65187–65205, 2021.
[7] H. Iida and M. N. A. Khalid, “Using games to study law of motions in mind,” IEEE Access, vol. 8, pp. 138701–138709, 2020.
[8] A. Ramadhan, H. Iida, and N. U. Maulidevi, “Game refinement theory and multiplayer games: case study using uno,” 2015.
[9] P. Anunpattana, M. N. A. Khalid, H. Iida, and W. Inchamnan, “Capturing potential impact of challenge-based gamification on gamified quizzing in the classroom,” Heliyon, vol. 7, no. 12, 2021.
[10] E. S. Tan and J. Jansz, “The game experience,” in Product experience, pp. 531–556, Elsevier, 2008.
[11] Wikipedia, “皮 爾 森 卡 方 檢 定.” https://zh.wikipedia.org/zh-tw/%E7%9A%AE%E7%88%BE%E6%A3%AE%E5%8D%A1%E6%96%B9%E6%AA%A2%E5%AE%9A, 2023.