Human-Machine Dialogue as a Stochastic Game

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

Type de document :

Communication dans un congrès avec actes

Titre :

Human-Machine Dialogue as a Stochastic Game

Auteur(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]

Titre de la manifestation scientifique :

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

Ville :

Prague

Pays :

République tchèque

Date de début de la manifestation scientifique :

2015-09-02

Date de publication :

2015-09

Discipline(s) HAL :

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

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Dialogue Homme-Robot

Collections :