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Author: Sugiyanto
Thesis Title: 策略階段控制 (SSBC):一種新穎的人工智能方法,用於玩大老二,具有不同的階段和策略
Strategic Stage-Based Control (SSBC): A Novel AI Method for Playing Big Two with Distinct Stages and Strategies
Advisor: 戴文凱
Wen-Kai Tai
Committee: 戴文凱
Wen-Kai Tai
Yi-Leh Wu
Yu-Chi Lai
Der-Lor Way
Ping-Lin Fan
Degree: 博士
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2024
Graduation Academic Year: 112
Language: 英文
Pages: 65
Keywords (in Chinese): AI代理大老二戰略階段控制
Keywords (in other languages): AI agent, Big Two, Strategic Stage-Based Control
Reference times: Clicks: 103Downloads: 0
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大老二(Big Two),一種在亞洲流行的棄牌型紙牌遊戲,通常由四名玩家進行。這項研究介紹了一種專門為玩大老二的AI代理設計的創新戰略階段控制(Strategic Stage-Based Control,SSBC)方法。這種SSBC方法能夠識別獲勝的舉動,並在不同階段指導戰略決策,包括開局、中盤和終盤。我們提出三個關鍵特徵來選擇最佳遊戲計劃:剩餘舉動次數、剩餘牌數和遊戲計劃得分。實驗結果表明,我們的戰略AI明顯優於隨機AI、傳統AI、基於規則的AI和人類玩家。此外,鑑於其在資源有限的情況下運作的能力,所提出的AI為小型遊戲工作室提供了一種可行的解決方案。

Big Two, a popular shedding-type card game from Asia, is typically played by four players. This research introduces a novel Strategic Stage-Based Control (SSBC) method specifically designed for an AI agent playing Big Two. This SSBC method can identify a winning move and guide strategic decision-making across distinct stages, including the opening, middlegame, and endgame. We propose three crucial features for selecting the optimal game plan: the number of remaining moves, the number of remaining cards, and the game plan score. Experimental results indicate that our strategic AI significantly outperforms randomized AI, conventional AI, rule-based AI, and human players. In addition, given its ability to operate with limited resources, the proposed AI presents a feasible solution for small-scale game studios.

Doctoral Dissertation Recommendation Form i Qualification Form by Doctoral Degree Examination Committee ii Abstract in Chinese iii Abstract in English iv Acknowledgements v Contents vi List of Figures ix List of Tables xi List of Algorithms xii Chapter 1. Introduction 1 1.1. Background and Motivation 1 1.2. Research Goals 2 1.3. Overview of Our Method 2 1.4. Contributions 3 1.5. Chapter Structure of This Dissertation 3 Chapter 2. Related Work 4 2.1. Big Two Card Game 4 2.2. AI Agents 7 2.3. Randomized AI 7 2.4. Conventional AI 8 2.5. Rule-based AI 10 2.6. Comparative Analysis of AI Agents 11 Chapter 3. Method 16 3.1. Defining Three Game Stages 16 3.2. Strategic Stage-Based Control (SSBC) Method 19 3.3. Scoring Combination 21 3.4. Opening Stage 23 3.4.1. Generating Opening Game Plan Profiles 23 3.4.2. Identifying a Winning Opening Move 24 3.4.3. Opening Strategy 26 3.5. Transition (Middlegame or Endgame) Stage 27 3.5.1. Generating Transition Game Plan Profiles 27 3.5.2. Identifying a Winning Move in a Control Position 28 3.5.3. Identifying a Winning Move in a Non-Control Position 30 3.6. Middlegame Stage 33 3.6.1. Middlegame Strategy in a Control Position 33 3.6.2. Middlegame Strategy in a Non-Control Position 35 3.6.3. High-Card Pressure Strategy 38 3.7. Endgame Stage 40 3.7.1. Endgame Strategy in a Control Position 40 3.7.2. Endgame Strategy in a Non-Control Position 41 Chapter 4. Experiment 44 4.1. Experiment Setup 44 4.2. Experimental Results 45 4.3. Experiment 1: Comparing the Performance of Strategic AI with Randomized AI 46 4.4. Experiment 2: Comparing the Performance of Strategic AI with Conventional AI 50 4.5. Experiment 3: Comparing the Performance of Strategic AI with Rule-based AI 52 4.6. Experiment 4: Comparing the Performance of Strategic AI with Human Players 54 Chapter 5. Conclusions and Future Work 59 5.1. Conclusions 59 5.2. Future Work 59 References 61

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