Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
The Knowledge Engineering Review
Agenda-based user simulation for bootstrapping a POMDP dialogue system
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Sample-efficient batch reinforcement learning for dialogue management optimization
ACM Transactions on Speech and Language Processing (TSLP)
Sample efficient on-line learning of optimal dialogue policies with kalman temporal differences
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Inverse reinforcement learning for interactive systems
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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There has been a lot of interest for user simulation in the field of spoken dialogue systems during the last decades. User simulation was first proposed to assess the performance of SDS before a public release. Since the late 90's, user simulation is also used for dialogue management optimisation. In this position paper, we focus on statistical methods for user simulation, their main advantages and draw-backs. We initiate a reflection about the utility of such methods and give some insights of what their future should be.