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題 名 | 使用機器學習理論建構遊戲中非玩家角色之情緒變化=Emotion Classification of Non-Player Character in Games Using Machine Learning Methods |
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作 者 | 王榮英; 李瑋翰; | 書刊名 | 龍華科技大學學報 |
卷 期 | 31 2011.12[民100.12] |
頁 次 | 頁61-72 |
分類號 | 312.2 |
關鍵詞 | 情緒; 類神經網路; 遊戲人工智慧; 支持向量機; Emotion; Neural network; Game AI; Support vector machine; |
語 文 | 中文(Chinese) |
中文摘要 | 近年來遊戲人工智慧有極大的進步,遊戲中非玩家控制角色其模擬人類的擬真度上也越來越逼真,但在人類情緒的模擬上確一直難有突破。現今遊戲模擬人類情緒的方法大多是使用有限狀態機,但有限狀態機局限於其線性變化之機制及有限的狀態,無法詮釋人類精巧的情緒變化。本研究先對有關人類情緒之研究做一分析與探討,而後將可實做之部分映射至遊戲之非玩家角色中。於本研究中使用目前廣泛用於各領域的機器學習理論,類神經網路和支持向量機,來模擬非玩家控制角色,學習產生情緒。遊戲中之中立角色以五個基本情緒分別為:愛、憤怒、恐懼、快樂以及厭惡,當作特徵值給予學習來模擬之。而遊戲中之敵對角色則以四個基本情緒,喜悅、痛苦、憤怒以及恐懼來模擬之。模擬的結果顯示無論是應用類神經網路或支持向量機,兩者之預測準確率皆超過 93%以上,故皆可替代傳統的有限狀態機,成功的讓非玩家控制角色有著情緒的變化,並且讓遊戲程式開發人員,除了傳統的有限狀態機之外,更多了其他選擇。 |
英文摘要 | In recent years, game artificial intelligence has been making great progress, and the simulation of human-like decision in non-player character is also more and more realistic. However, the simulated human emotions in game have been few studies in these days. This is because of today's game simulation of human emotion mostly uses the finite state machine, and the finite number of inputs and states makes it difficult to apply it to simulated the complicated human motions. In this paper, we first review and summarize the theories and researches on human emotions. Then we select the appropriate items from human emotions to implement them in game. We use two popular machine learning methods, neural network and support vector machine, to simulate the emotions of non-player character. We use five basic emotions to simulate the neutral role in game. There are love, anger, fear, happiness and disgust. Meanwhile, joy, pain, anger and fear are four basic emotions using in game’s enemy role. The experiment results show that the applications for both neural network and support vector machine achieve the prediction accuracy rate of over 93%. In other words, both of them can successful using in the simulation of emotion in games, and can easily replace the traditional method of finite state machine. |
本系統中英文摘要資訊取自各篇刊載內容。