Planning and Acting under Uncertainty: A New Model for Spoken Dialogue
  Systems
  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.
