Planning and acting in partially observable stochastic domains
Artificial Intelligence
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Training a real-world POMDP-based dialogue system
NAACL-HLT-Dialog '07 Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies
Scaling POMDPs for Spoken Dialog Management
IEEE Transactions on Audio, Speech, and Language Processing
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
SETQA-NLP '09 Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing
Optimising information presentation for spoken dialogue systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Natural language generation as planning under uncertainty for spoken dialogue systems
Empirical methods in natural language generation
Gaussian Processes for POMDP-Based Dialogue Manager Optimization
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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This paper investigates the claim that a dialogue manager modelled as a Partially Observable Markov Decision Process (POMDP) can achieve improved robustness to noise compared to conventional state-based dialogue managers. Using the Hidden Information State (HIS) POMDP dialogue manager as an exemplar, and an MDP-based dialogue manager as a baseline, evaluation results are presented for both simulated and real dialogues in a Tourist Information Domain. The results on the simulated data show that the inherent ability to model uncertainty, allows the POMDP model to exploit alternative hypotheses from the speech understanding system. The results obtained from a user trial show that the HIS system with a trained policy performed significantly better than the MDP baseline.