Planning and Acting under Uncertainty: A New Model for Spoken Dialogue Systems
Bo Zhang, Qingsheng Cai, Jianfeng Mao, Baining Guo
Uncertainty plays a central role in spoken dialogue systems. Some stochastic models like Markov decision process (MDP) are used to model the dialogue manager. But the partially observable system state and user intention hinder the natural representation of the dialogue state. MDP-based system degrades fast when uncertainty about a user's intention increases. We propose a novel dialogue model based on the partially observable Markov decision process (POMDP). We use hidden system states and user intentions as the state set, parser results and low-level information as the observation set, domain actions and dialogue repair actions as the action set. Here the low-level information is extracted from different input modals, including speech, keyboard, mouse, etc., using Bayesian networks. Because of the limitation of the exact algorithms, we focus on heuristic approximation algorithms and their applicability in POMDP for dialogue management. We also propose two methods for grid point selection in grid-based approximation algorithms.
PDF Link: /papers/01/p572-zhang.pdf
AUTHOR = "Bo Zhang
and Qingsheng Cai and Jianfeng Mao and Baining Guo",
TITLE = "Planning and Acting under Uncertainty: A New Model for Spoken Dialogue Systems",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "572--579"