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Human-Machine Dialogue as a Stochastic Game

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.
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Contributor : Olivier Pietquin Connect in order to contact the contributor
Submitted on : Friday, November 6, 2015 - 6:35:03 PM
Last modification on : Thursday, January 20, 2022 - 4:17:13 PM
Long-term archiving on: : Monday, February 8, 2016 - 1:02:27 PM


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  • HAL Id : hal-01225848, version 1



Merwan Barlier, Julien Perolat, Romain Laroche, Olivier Pietquin. Human-Machine Dialogue as a Stochastic Game. 16th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2015), Sep 2015, Prague, Czech Republic. ⟨hal-01225848⟩



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