An Embodied Conversational Agent (ECA) is an intelligent agent that works as the front end of software applications to interact with users through verbal/nonverbal expressions and to provide online assistance without the limits of time, location, and language. To help to improve the experience of human-computer interaction, there is an increasing need to empower ECA with not only the realistic look of its human counterparts but also a higher level of intelligence. This thesis first highlights the main topics related to the construction of ECA, including different approaches of dialogue management, and then discusses existing techniques of trend analysis for its application in user classification. As a further refinement and enhancement to prior work on ECA, this thesis research proposes a cohesive framework to integrate emotion-based facial animation with improved intention discovery. In addition, a machine learning technique is introduced to support sentiment analysis for the adjustment of policy design in POMDP-based dialogue management. The proposed research work is going to improve the accuracy of intention discovery while reducing the length of dialogues.

中文翻译：

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基于 POMDP 对话管理的机器学习意图发现改进方法

Embodied Conversational Agent (ECA) 是一种智能代理，它作为软件应用程序的前端，通过语言/非语言表达与用户进行交互，并提供不受时间、地点和语言限制的在线帮助。为了帮助改善人机交互体验，越来越需要赋予 ECA 不仅具有其人类对应物的逼真外观，而且具有更高水平的智能。本文首先重点介绍了与 ECA 构建相关的主要主题，包括对话管理的不同方法，然后讨论了现有趋势分析技术在用户分类中的应用。作为对非洲经委会先前工作的进一步完善和加强，本论文研究提出了一个内聚框架，将基于情感的面部动画与改进的意图发现相结合。此外，引入了机器学习技术来支持情绪分析，以调整基于 POMDP 的对话管理中的策略设计。拟议的研究工作将提高意图发现的准确性，同时减少对话的长度。