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研究生:謝皓閔
研究生(外文):Hao-Min Hsieh
論文名稱:小精靈遊戲之策略自動產生與適應調整
論文名稱(外文):Generation of Adaptive Opponents for a Predator-Prey Game
指導教授:王玲玲王玲玲引用關係
指導教授(外文):Ling-Ling Wang
學位類別:碩士
校院名稱:亞洲大學
系所名稱:資訊與設計學系碩士班
學門:設計學門
學類:視覺傳達設計學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:38
中文關鍵詞:電腦對手遊戲人工智慧模糊規則基因演算法動態難度調整獵食者與獵物遊戲
外文關鍵詞:computer-controlled opponentgame artificial intelligencefuzzy rulegenetic algorithmdynamic difficulty adjustmentpredator-prey game
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在多數電腦遊戲中,電腦對手通常是以人工撰寫的腳本來控制。然而撰寫腳本經常得耗費遊戲開發者許多時間與精力,同時此過程亦是冗長與乏味的。此外,電腦對手的遊戲策略通常相當有限且固定,因此當玩家玩了一段時間之後,便能察覺其行為模式的重覆性,於是感覺失去挑戰性而覺得無趣。再者,遊戲中固定的難易度設定亦無法滿足市場上各式各樣、不同程度的玩家。為了克服上述問題,已有研究者提出以離線學習方法來自動產生電腦對手之策略,以省去人工撰寫腳本的時間成本。另外則有研究提出線上學習方法以進行動態難度調整,使遊戲的難易度能自動調整到符合玩家的程度。然而這些方法大多使用類神經網路來學習遊戲策略,因此有不易理解及難以維護的缺點。另外許多動態難度調整技術之效率不佳,因而難以實際運用到商業遊戲中。為了改進現有方法的缺點,本研究提出一套以模糊邏輯為基礎的離線及線上學習方法。在離線學習的部份,本研究提出以基因演算法自動產生一組模糊規則庫,以作為電腦對手之策略。而線上學習的部份,本研究則提出以機率方法來調整電腦對手之程度,使得遊戲難度能貼近玩家的程度,以自動達到遊戲平衡。實驗結果證明了此一離線學習方法所產生的模糊規則庫,可有效地控制電腦對手。同時實驗結果亦證明本研究之線上學習方法可有效率地調整遊戲難易度,並且適用各種不同程度的玩家。
Computer-controlled opponents in a computer game are often controlled by a lot of scripts. However, writing scripts needs a long period of trial and error and is usually tedious to game developers. Besides, the tactics of computer-controlled opponents are often fixed and limited. The behavioral repetition of computer-controlled opponents reduces human player’s enjoyment during gameplay. Furthermore, in most commercial games, the predefined fixed difficulty levels cannot satisfy players of varied experiences in gameplay. To solve these problems, some researchers proposed offline learning approaches to automatically generating game tactics of computer-controlled opponents, and online learning approaches to realizing dynamic difficulty adjustment. However, most of the offline and the online learning approaches are neural network-based, and their major drawback is the lack of transparency which increases the difficulty of maintenance for game developers. Furthermore, the adaptation efficiency of those online learning techniques is not enough for the games played with human players. In this study we propose both offline and online learning approaches. In offline learning, we apply a genetic algorithm to generate a fuzzy rulebase, which is used as the game tactics during gameplay to guide the computer-controlled opponents. In online learning, we use a probabilistic method to adapt the game tactics to the player. The difficulty level of the game can be adapted according to the player’s preference for challenge. The experimental results show the effectiveness of the offline evolved rulebases and the feasibility of the proposed online learning approach on dynamic difficulty adjustment. The research results will be of help to game developers for creating game AI in their games.
Abstract in Chinese i
Abstract in English ii
Acknowledgements iii
Table of Contents iv
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
Chapter 2 The Dead End World 6
Chapter 3 Offline Generation of Game Tactics 9
3.1 Specification of Fuzzy Variables 9
3.2 Evolution of Fuzzy Rules 12
3.2.1 Encoding of Chromosome 12
3.2.2 Fitness Value of Chromosome 14
3.2.3 Evolution of Chromosome 16
3.3 Experimental Results 17
Chapter 4 Online Adaptation of Game Tactics 21
4.1 The Online Adapting Approach 21
4.2 Experimental Results 25
Chapter 5 Conclusions 34
References 36
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