Human-Machine Dialogue as a Stochastic Game

Barlier, Merwan; Perolat, Julien; Laroche, Romain; Pietquin, Olivier

Document type :

Communication dans un congrès avec actes

Title :

Human-Machine Dialogue as a Stochastic Game

Author(s) :

Barlier, Merwan [Auteur]
Sequential Learning [SEQUEL]
Orange Labs [Issy les Moulineaux]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Perolat, Julien [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille, Sciences et Technologies
Laroche, Romain [Auteur]
Orange Labs [Issy les Moulineaux]
Pietquin, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille, Sciences et Technologies
Institut Universitaire de France [IUF]
Sequential Learning [SEQUEL]

Conference title :

16th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2015)

City :

Prague

Country :

République tchèque

Start date of the conference :

2015-09-02

Publication date :

2015-09

HAL domain(s) :

Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Interface homme-machine [cs.HC]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Dialogue Homme-Robot

Collections :