Abstract : In this paper, an original framework to model human-machine spoken dialogues is proposed to deal with co-adaptation between users and Spoken Dialogue Systems in non-cooperative tasks. The conversation is modeled as a Stochastic Game: both the user and the system have their own preferences but have to come up with an agreement to solve a non-cooperative task. They are jointly trained so the Dialogue Manager learns the optimal strategy against the best possible user. Results obtained by simulation show that non-trivial strategies are learned and that this framework is suitable for dialogue modeling.
https://hal.inria.fr/hal-01225848
Contributeur : Olivier Pietquin
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Soumis le : vendredi 6 novembre 2015 - 18:35:03
Dernière modification le : mardi 3 juillet 2018 - 11:21:29
Document(s) archivé(s) le : lundi 8 février 2016 - 13:02:27
