Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
User evaluation of the MASK kiosk
Speech Communication
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance
User Modeling and User-Adapted Interaction
Exploratory search: from finding to understanding
Communications of the ACM - Supporting exploratory search
The LIMSI ARISE system for train travel information
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Neurocomputing
Dialogue management in the Mercury flight reservation system
ConversationalSys '00 Proceedings of the ANLP-NAACL 2000 Workshop on Conversational Systems
Natural language generation as planning under uncertainty for spoken dialogue systems
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
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
Annotating dialogue acts to construct dialogue systems for consulting
ALR7 Proceedings of the 7th Workshop on Asian Language Resources
Evaluating the effectiveness of information presentation in a full end-to-end dialogue system
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Scaling POMDPs for Spoken Dialog Management
IEEE Transactions on Audio, Speech, and Language Processing
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
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This article presents a user model for user simulation and a system state representation in spoken decision support dialogue systems. When selecting from a group of alternatives, users apply different decision-making criteria with different priorities. At the beginning of the dialogue, however, users often do not have a definite goal or criteria in which they place value, thus they can learn about new features while interacting with the system and accordingly create new criteria. In this article, we present a user model and dialogue state representation that accommodate these patterns by considering the user's knowledge and preferences. To estimate the parameters used in the user model, we implemented a trial sightseeing guidance system, collected dialogue data, and trained a user simulator. Since the user parameters are not observable from the system, the dialogue is modeled as a partially observable Markov decision process (POMDP), and a dialogue state representation was introduced based on the model. We then optimized its dialogue strategy so that users can make better choices. The dialogue strategy is evaluated using a user simulator trained from a large number of dialogues collected using a trial dialogue system.