Autonomous Agents and Multi-Agent Systems
The Knowledge Engineering Review
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Testing the performance of spoken dialogue systems by means of an artificially simulated user
Artificial Intelligence Review
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
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User simulation has become an important trend of research in the field of spoken dialog systems because collecting and annotating real man-machine interactions with users is often expensive and time consuming. Yet, such data are generally required for designing and assessing efficient dialog systems. The general problem of user simulation is thus to produce as many as necessary natural, various and consistent interactions from as few data as possible. In this paper, is proposed a user simulation method based on Bayesian Networks (BN) that is able to produce consistent interactions in terms of user goal and dialog history but also to simulate the grounding process that often appears in human-human interactions. The BN is trained on a database of 1234 human-machine dialogs in the TownInfo domain (a tourist information application). Experiments with a state-of-the-art dialog system (REALL-DUDE/DIPPER/OAA) have been realized and promising results are presented.