在這篇論文中，我們用不同的情境資料去釋義互動戲劇中的玩家動作。首先，我們提出一個針對使用者動作的正規表示法，叫做動作正規表示法(Action Regular Expression)，動作正規表示法可以視為傳統用於編譯器的正規表示法的延伸，只是針對玩家動作的特性加上新的運算子。在擷取和辨識使用者的身體關節點方面，我們使用微軟的Kinect 感應器，針對我們的應用，我們用動作正規表示法定義了八個動作；動作辨識的實驗是由六個受測者針對不同模式去反覆執行這八個動作，而最後得到的平均準確率是86%。辨識出動作後，一串動作背後的意圖由動作釋義器(action interpreter)來決定，動作釋義器包涵了一個plan library和分類器，這個plan library內建有75 個plan，我們使用支持向量機(support vector machine)去學習這個plan library，為了找到支持向量機學習時的最佳參數，我們跑了110 筆不同的參數組合，而最後得到的最佳參數可讓學習出的model擁有94.74%的高準確率。

In this thesis, a context-dependent action interpreter for interactive storytelling is proposed. At first, a regular expression for actions, named Action Regular Expression (ARE), is proposed; in fact, ARE can be thought as an extension of traditional regular expression, new operators are defined to deal with the properties of actions. To acquire and recognize user’s body joints, a Microsoft Kinect sensor is used; then eight actions are defined and recognized via ARE. The experiment runs on six subjects and the obtained total average accuracy is 86%. To interpret user’s actions, an action interpreter is defined which contains a plan library and the classifier. The plan library with 75 similar plans is built, and due to the ability of maximizing data margins, support vector machine is applied to learn the plan library. To get the optimal parameters for the support vector machine, a set of 110 combinations are tried; at final, a trained model with 94.74% accuracy is obtained while choosing the optimal parameters.

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