本論文提出一個具有真人輔導學習機制的客製化對話系統架構，將其應用於銀髮族照護系統。該系統可根據不同使用者可以做出口語對話、影音媒體播放、資訊檢索、協助聯絡等行為回饋。在對話系統原型設計採用有限狀態基礎模型實現，並藉由提出的PRCBRB句子相似度演算法實現快速的對話系統架構。於對話策略階段根據Wizard of Oz理論加入真人輔助學習機制，此機制可將系統無法處理的對話狀況交給支援者處理，系統紀錄這個過程，並對於對話系統劇本修改。在客製化機制部分，本論文自訂了個人資訊知識表示格式及兩種知識檢索方法，可讓回饋動作根據使用者的登入資訊做出調整，達到客製化功能。最後於介面設計上則根據銀髮族對於科技的使用習慣做出使用者介面調整，實務層面達到相映的成果。實驗部分，對於PRCBRB演算法做出分析，以MCDC語料庫做為測試資料，在inside-test下，正確率可達到97%，運算效能則相較於其他類似演算法快5倍。在Outside-test的部分，實際對於31位使用者(其中包含10位65歲以上的銀髮族)使用狀況評估12種使用者需求任務的任務達成率，一般使用者可達到86.7%，銀髮族為64%，在MOS平均主觀評測分數方面，一般使用者達到4.4，銀髮族可達到為4.3，在實務上是適用於銀髮族的。

In this thesis, we proposed a customized dialogue system with human assisted learning mechanism is proposed to realize the orange technologies of warming-care system for seniors. The system has the capability of spoken dialogue, audio and video media playing, information retrieval and remote assistance connection etc. Moreover, the system feedback strategy is adapted according to the enrolled user information for customized request. The dialogue system prototype is designed based on Finite-state-script model and the proposed POS-Reference-Column-Based-Row-Based (PRCBRB) sentence similarity algorithm. Human assisted learning mechanism is added to stage of dialogue strategy according to Wizard-of-Oz design methodology. If the system fails to recognize how to respond the user request, the system will automatically call for assistance from supporter. Then feedback of supporter is relayed to user by system. Assistance of supporter is converted into the new script and added to script database by learning mechanism. The aforementioned customized mechanism is fundamentally realized by the following proposed techniques, including, the personal information knowledge representation format and two kinds of knowledge retrieval methods. In the experiments, the Mandarin Conversational Dialogue Corpus (MCDC) corpus is applied for testing and analyzing the efficiency of the PRCBRB algorithm. In the inside-test, the accuracy can achieve to 97.3% accuracy and the computation cost is 5 times faster than the other algorithms. In the outside-test, the task completion rate (TCR) of 12 kinds of tasks for 31 users including 10 seniors over 65 are evaluated. For the general user and the senior users, TCR can be reached to 86.7% and 64%, respectively. In Mean Opinion Score evaluation, the average MOS score is up to 4.4 for the general use, and up to 4.3 for the senior user. In light of above experimental results, the proposed system is proven the usability for the seniors in the real life.

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