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研究生:謝沂蓁
研究生(外文):Hsieh Yi Chen
論文名稱:運用群體智慧發展遊戲群聚包圍策略之研究
論文名稱(外文):Developing Investing Strategy with Swarm Intelligence in Game
指導教授:李昆翰李昆翰引用關係
學位類別:碩士
校院名稱:臺北市立教育大學
系所名稱:資訊科學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:65
中文關鍵詞:群聚包圍策略群體智慧粒子群最佳化遊戲
外文關鍵詞:Investing StrategySwarm IntelligenceParticle Swarm OptimizationGames
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電腦遊戲隨著電腦科技發展至今,已成為人類生活中的一部分,為了加強遊戲的樂趣及挑戰性,在遊戲中已漸漸應用人工智慧(artificial intelligence, AI)增添樂趣。本篇所要探討的內容為設計一遊戲演算法,使具Reynolds所提出類鳥體(Boids)特性的群體,在遊戲情境中受到單一敵人攻擊時,運用群體智慧(swarm intelligence)產生恫嚇敵人攻擊的策略。因此研究主要的目的為當群體面對敵人時,群體移動除具趨向行為(steering behavior)外,還希望透過粒子群最佳化演算法(particle swarm optimization, PSO)表現出群體智慧,使群體產生包圍現象並牽制敵人的攻擊能力,利用「群」的優勢影響攻擊者攻擊能力,提升群體生存個數與延長生存時間。研究中因應PSO演算法選擇兩種適應函數模擬,分別比較不同適應函數所產生的群體,在不同疊代次數下與敵人互動的表現如何,模擬結果顯示PSO演算法的加入可提高群體生存個數、時間,並且使群體與敵人互動的過程符合逼真感(believability)指標。
關鍵字:群聚包圍策略、群體智慧、粒子群最佳化、遊戲

Today, owing to the development of the computer technology, computer games have played an important role in our leisure life. For enhancing the challenge and attraction for the game players, the quality and complexity of AI (artificial intelligence) used in computer games are gradually increasing. The aim of this study is to propose a game AI for a flock of individuals that use swarm intelligence to invest an enemy successfully. Specifically, the researcher considers that a flock of individuals is not only with Reynolds’s boids steering behavior but also with attributes of investing an enemy by particle swarm optimization. Once the enemy is invested by the flock of individuals, its ability of chasing and attacking becomes decreasing. The life span of the flock becomes increasing. Different fitness functions and iterations are applied in the experiment. From this experiment, it shows that the flock of individuals becomes much stronger, and the believability improves by investing strategy. Therefore, it appears that investing strategy is a benefit to games.
Keywords: Investing Strategy, Swarm Intelligence, Particle Swarm Optimization, Games 

謝 誌 I
摘 要 II
ABSTRACT III
目 次 IV
圖 次 VI
表 次 VIII
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的與問題 5
第三節 研究流程與架構 6
第二章 文獻探討 9
第一節 遊戲案例 9
第二節 群體移動 14
第三節 遊戲逼真感 23
第三章 粒子群最佳化演算法 27
第一節 PSO概述 27
第二節 PSO概念特色 27
第三節 PSO基本技術 28
第四節 PSO應用類型 31
第四章 群聚包圍策略 33
第一節 實驗情境 33
第二節 策略演算法 34
第三節 適應函數的選擇 36
第四節 模擬架構 38
第五章 結果與討論 45
第一節 模擬結果與討論 45
第二節 適應值比較與討論 53
第三節 應用實益 55
第六章 結論與建議 57
參考文獻 59

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