• Corpus ID: 196196701

An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management

  title={An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management},
  author={Ruturaj Rajendrakumar Raval},
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… 


Emotion Animation of Embodied Conversational Agents with Contextual Control Model
  • Xiaobu Yuan, R. Vijayarangan
  • Computer Science
    2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing
  • 2013
A modified POMDP (Partially Observable Markov Decision Processes) model is suggested for the introduction of system's response time into the control of dialogue management, and a novel algorithm is created to direct conversation in different contextual control modes.
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A new dialogue management model for affective dialogue system, which aims to provide a service of information inquiry and affective interaction, is proposed and summarized as a user model, which is helpful for the system to inference and predict the user's internal states.
A modified approach of POMDP-based dialogue management
  • Xiaobu Yuan, L. Bian
  • Computer Science
    2010 IEEE International Conference on Robotics and Biomimetics
  • 2010
A modified approach of POMDP-based dialogue management is proposed, which introduces belief history into the planning process, and uses not only the current but also the previous belief state for the determination of actions.
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Feudal Reinforcement Learning for Dialogue Management in Large Domains
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A set of challenging simulated environments for dialogue model development and evaluation is proposed and a number of representative parametric algorithms, namely deep reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic are investigated and compared to a non-parametric model, GP-SARSA.
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