Conversation as Action Under Uncertainty
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
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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