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
Contributor : Olivier Pietquin
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Submitted on : Friday, November 6, 2015 - 6:35:03 PM
Last modification on : Thursday, April 4, 2019 - 10:18:05 AM
Long-term archiving on : Monday, February 8, 2016 - 1:02:27 PM
