An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management
@article{Raval2020AnIA, title={An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management}, author={Ruturaj Rajendrakumar Raval}, journal={ArXiv}, year={2020}, volume={abs/2009.09354} }
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…Â
Figures and Tables from this paper
figure 1 figure 2 figure 3 figure 4 figure 5 table 5 figure 6 table 6 figure 7 table 7 figure 8 table 8 figure 9 table 9 table 10 figure 11 table 11 figure 12 table 12 figure 13 table 13 figure 14 table 14 figure 15 figure 16 table 16 figure 17 table 17 figure 18 table 18 figure 19 table 19 figure 20 table 20 figure 21 table 21 figure 22 table 22 figure 23 table 23 figure 24 figure 25 figure 26 figure 27 figure 28 figure 29 figure 30 figure 31 figure 32 figure 33 figure 34 figure 35 figure 40 figure 62 figure 72 figure 73 figure 74 figure 75 figure 76 figure 77 figure 79 figure 80 figure 82 figure 83 figure 84
References
SHOWING 1-10 OF 102 REFERENCES
Emotion Animation of Embodied Conversational Agents with Contextual Control Model
- Computer Science2013 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.
TFSM-based dialogue management model framework for affective dialogue systems
- Computer Science
- 2015
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
- Computer Science2010 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.
Toward affective dialogue management using partially observable Markov decision processes
- Computer Science
- 2008
This thesis presents a novel approach to developing interfaces for multi-application systems which are dialogue systems that allow the user to navigate between a large set of applications smoothly and transparently and proposes a tractable hybrid DDN-POMDP method to tackle many of these scalability problems.
Feudal Reinforcement Learning for Dialogue Management in Large Domains
- Computer ScienceNAACL
- 2018
A novel Dialogue Management architecture, based on Feudal RL, is proposed, which decomposes the decision into two steps; a first step where a master policy selects a subset of primitive actions, and a secondstep where a primitive action is chosen from the selected subset.
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning
- Computer ScienceSIGDIAL Conference
- 2017
A new method for hierarchical reinforcement learning using the option framework is proposed and it is shown that the proposed architecture learns faster and arrives at a better policy than the existing flat ones do.
End-to-End Task-Completion Neural Dialogue Systems
- Computer ScienceIJCNLP
- 2017
The end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management
- Computer ScienceArXiv
- 2017
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.
Dialogue manager domain adaptation using Gaussian process reinforcement learning
- Computer ScienceComput. Speech Lang.
- 2017
Strategic Dialogue Management via Deep Reinforcement Learning
- Computer ScienceNIPS 2015
- 2015
A successful application of Deep Reinforcement Learning with a high-dimensional state space to the strategic board game of Settlers of Catan is described, which supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities.