文本人物重要性預測: 以協助開發歷史模擬遊戲為例

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張偉康(Wei-Kung Zhang) 查詢紙本館藏

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資訊工程學系在職專班

論文名稱

文本人物重要性預測: 以協助開發歷史模擬遊戲為例
(Predicting Textual Character Importance: A Case Study in Assisting Historical Simulation Game Development)

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至系統瀏覽論文 (2025-12-31以後開放)

摘要(中)

本文提出了一種創新的方法，利用人工智能和數據挖掘技術設計歷史模擬遊戲中的角色。我們專注於從影響力極大的歷史文本《三國演義》 (RTK) 中提取信息，以協助歷史模擬遊戲的開發。該研究利用自然語言處理 (NLP) 和命名實體識別 (NER) 從 RTK 文本中提取命名實體，形成知識圖譜數據集。機器學習基礎的節點重要性估計模型在這個數據集上進行訓練，以預測 RTK 中的角色重要性。我們將此模型的預測結果與參與者在調查中選擇的最愛角色進行對比，發現二者之間有很大的相似性。這也是一種新的框架，使歷史模擬遊戲的開發者可以基於中文文本預測角色的重要性。我們的結果突顯出這些方法在提高角色設計和敘事發展方面的潛力，從而提供了更沉浸式和吸引人的遊戲體驗。

摘要(英)

This paper presents an innovative approach to character design in historical simulation games, leveraging artificial intelligence and data mining techniques. We focus on extracting information from an influential historical text, Romance of the Three Kingdoms(RTK) to assist in the development of historical simulation games. The study uses Natural Language Processing (NLP) and Named Entity Recognition (NER) to extract Named Entities from RTK text, forming a knowledge graph dataset. The machine learning-based node importance estimation model is trained on this dataset to predict character significance in RTK. We contrast the predictions of this model with the favorite characters selected by participants in a survey, discovering a strong similarity. This is also a new framework that enables developers of historical simulation games to predict the significance of characters based on Chinese text. Our results highlight the potential of these methodologies in enhancing character design and narrative development, thereby offering a more immersive and engaging gaming experience.

關鍵字(中)

★ 歷史模擬遊戲
★ 角色設計
★ 點重要性估計
★ 市場研究

關鍵字(英)

★ historical simulation games
★ character design
★ node importance estimation
★ market research

論文目次

中文摘要 v
Abstract vi
Contents vii
List of Figures x
List of Tables xi
1 Introduction 1
1.1 Introduction to Character Design in Historical Simulation Games . . . . . 1
1.2 The Crucial Role of Character Design in Historical Simulation Games . . 1
1.3 Ethical Considerations in Character Design . . . . . . . . . . . . . . . . 2
1.4 Implications of Inaccurate Character Design . . . . . . . . . . . . . . . . 2
1.5 The Need for a Scientific Approach in Character Design . . . . . . . . . 2
1.6 Future of Character Design in Historical Simulation Games . . . . . . . . 3
1.7 Potential of Data Mining and Machine Learning in Historical Simulation
Game Character Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.8 A Novel Approach to Character Importance Estimation . . . . . . . . . . 4
1.9 Define Concept: Definitions of Person Importance in Historical Simulation Game Development . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Related Works 7
2.1 Historical Simulation Game Development . . . . . . . . . . . . . . . . . 7
vii2.2 Social Network Data Analysis of RTK . . . . . . . . . . . . . . . . . . . 8
2.3 Node Importance Estimation . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Historical Perspective on NIE . . . . . . . . . . . . . . . . . . . . . . . 9
2.5 Methodological Approaches in NIE . . . . . . . . . . . . . . . . . . . . 9
2.6 Random walk algorithms in NIE . . . . . . . . . . . . . . . . . . . . . . 10
2.7 Machine Learning in NIE . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.8 Our Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.9 Potential Impact of Our Research . . . . . . . . . . . . . . . . . . . . . . 11
3 Methods 13
3.1 Research Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Framework Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Nickname Dataset and Knowledge Graph . . . . . . . . . . . . . . . . . 16
3.3.1 Nickname Dataset . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3.2 Knowledge Graph . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4 Node Importance Estimation Model Training . . . . . . . . . . . . . . . 18
3.5 Survey Research Question Design . . . . . . . . . . . . . . . . . . . . . 19
3.6 Data Collection Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.7 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.8 Evaluation Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.9 Evaluation Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Results 24
4.1 Nickname Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.2 Knowledge Graph of RTK . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.4 Training and Evaluation Setup . . . . . . . . . . . . . . . . . . . . . . . 27
4.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5 Discussion 29
5.1 Descriptive statistics for samples . . . . . . . . . . . . . . . . . . . . . . 29
viii5.2 Reliability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.3 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5.4 Case Study: The three major battles of RTK . . . . . . . . . . . . . . . . 37
6 Conclusion 40
6.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Bibliography 42

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指導教授

蔡宗翰

審核日期

2023-7-26

推文