Planning and Acting Under Uncertainty: A New Model for Spoken Dialogue System

Abstract

Uncertainty plays a central role in spoken dialogue systems. Some stochastic models like the Markov decision process (MDP) are used to model the dialogue manager. But the partially observable system state and user intentions hinder the natural representation of the dialogue state. A MDP-based system degrades quickly 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, and domain actions and dialogue repair actions as the action set. Here, low-level information is extracted from different input modalities, 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 algorithms