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研究生:方永平
研究生(外文):Yung-Ping Fang
論文名稱:應用模糊增強式學習技術於數位遊戲之研究
論文名稱(外文):Applying Fuzzy Reinforcement Learning in Digital Games
指導教授:丁一賢丁一賢引用關係
指導教授(外文):I-Hsien Ting
學位類別:碩士
校院名稱:國立高雄大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:63
中文關鍵詞:數位遊戲遊戲人工智慧增強式學習模糊理論
外文關鍵詞:digital gamesartificial intelligence for gamesreinforcement learningfuzzy theory
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遊戲是人類生活上不可或缺的一項活動,近年來科技的蓬勃發展也帶起了數位遊戲產業的龐大市場,數位遊戲會吸引人的原因除了其聲光效果之外,遊戲中玩家與非玩家角色的互動也是一個非常重要的因素,為遊戲中的非玩家角色加入人工智慧技術可以讓這些非玩家角色具有人類的思考能力,也因此可以讓遊戲與玩家的互動性更佳。

數位遊戲中的環境是不斷在改變的,因此要為非玩家角色加入人工智慧通常會是一個具有挑戰性的問題。在本研究中要將增強式學習技術應用在數位遊戲的非玩家角色中,增強式學習技術是一種非監督式的學習方式,通常用於機械的自動化學習過程中,增強式學習技術是一個不斷的試誤的學習過程,並且代理人會藉由去探索新環境來改變其行為,是一種適用於未知環境下的學習方法。

要將增強式學習技術應用在數位遊戲的非玩家角色中,其中最大的困難就在於增強式學習必須要經過一段很久的時間去試誤學習,因此本研究利用模糊理論去改善傳統增強式學習的效率,在實驗的過程中,成功的用模糊獎懲取代固定獎懲,讓學習的速度加快,從實驗結果中也可以看出獎懲機制對於增強式學習結果的好壞有很大的影響,不同類型的遊戲會需要不同的獎懲設定,找到適合的獎懲機制就能讓數位遊戲實際應用的可能性提高。
Game is one of the indispensable activities for humanity lives. In recent years, the development of technology also brought huge market to game industry. One of the appealing reasons in game is that the player can interact with the non-player-characters in game. Artificial intelligence is very important for these non-player-characters due to Artificial intelligence can let non-player-characters have more interactions with players.

The environment in digital games is changing continuously, so it is a challenge to add artificial intelligence in non-player-characters. In this research, we would like to use reinforcement learning in non-player-characters’ artificial intelligence. Reinforcement learning is a un-supervise learning method, and it is usually used in automatic machine learning process. Reinforcement learning is a trial-and-error process, and the agent will change his actions by exploring the new environment.

The most difficult to apply Reinforcement learning in digital games is that the method requires a long learning time . In this research, we use fuzzy theory to increase the learning efficiency. The results of this research experiment also prove the improvement of learning efficiency through using fuzzy reward to replace fixed reward. Different types of game need different settings of reward. In practice, the probability to apply reinforcement learning in digital games can be enhanced once the suitable reward mechanism has been found out.
第1章 緒論
1.1 背景
1.2 動機與目的
1.3 研究假設
1.4 研究流程與論文架構
第2章 文獻探討
2.1 數位遊戲
2.2 遊戲人工智慧
2.3 增強式學習
2.4 本章小結
第3章 模糊增強式學習
3.1 傳統增強式學習的缺點
3.2 應用模糊理論於增強式學習之中
3.3 模糊增強式學習之運算
第4章 實驗設計與實驗結果
4.1 實驗設計
4.2 實驗環境與遊戲設計
4.4 前導實驗
4.5 實驗結果
4.6 遊戲呈現與遊戲結果
4.7 如何實際應用模糊增強式學習
4.8 本章小結
第5章 結論
5.1 摘要
5.2 結論
5.3 研究限制與未來研究
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