An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management
This work addresses the need for more intelligent human-computer interaction in ECAs, though it appears incremental as a refinement of prior techniques.
The research tackled improving intention discovery in POMDP-based dialogue management for Embodied Conversational Agents, proposing a cohesive framework that integrates emotion-based facial animation and machine learning for sentiment analysis, aiming to enhance accuracy and reduce dialogue length.
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.