Imitation Learning Applied to Embodied ...
Type de document :
Communication dans un congrès avec actes
Titre :
Imitation Learning Applied to Embodied Conversational Agents
Auteur(s) :
Piot, Bilal [Auteur]
Université de Lille, Sciences Humaines et Sociales
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Geist, Matthieu [Auteur]
CentraleSupélec
Georgia Tech Lorraine [Metz]
Pietquin, Olivier [Auteur]
Sequential Learning [SEQUEL]
Institut Universitaire de France [IUF]
Université de Lille, Sciences et Technologies
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille, Sciences Humaines et Sociales
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Geist, Matthieu [Auteur]
CentraleSupélec
Georgia Tech Lorraine [Metz]
Pietquin, Olivier [Auteur]
Sequential Learning [SEQUEL]
Institut Universitaire de France [IUF]
Université de Lille, Sciences et Technologies
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Éditeur(s) ou directeur(s) scientifique(s) :
JMLR Workshop and Conference Proceedings
Titre de la manifestation scientifique :
4th Workshop on Machine Learning for Interactive Systems (MLIS 2015)
Ville :
Lille
Pays :
France
Date de début de la manifestation scientifique :
2015-07-11
Titre de la revue :
Proceedings of the 4th Workshop on Machine Learning for Interactive Systems
Discipline(s) HAL :
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Interface homme-machine [cs.HC]
Informatique [cs]/Interface homme-machine [cs.HC]
Résumé en anglais : [en]
Embodied Conversational Agents (ECAs) are emerging as a key component to allow human interact with machines. Applications are numerous and ECAs can reduce the aversion to interact with a machine by providing user-friendly ...
Lire la suite >Embodied Conversational Agents (ECAs) are emerging as a key component to allow human interact with machines. Applications are numerous and ECAs can reduce the aversion to interact with a machine by providing user-friendly interfaces. Yet, ECAs are still unable to produce social signals appropriately during their interaction with humans, which tends to make the interaction less instinctive. Especially, very little attention has been paid to the use of laughter in human-avatar interactions despite the crucial role played by laughter in human-human interaction. In this paper, methods for predicting when and how to laugh during an interaction for an ECA are proposed. Different Imitation Learning (also known as Apprenticeship Learning) algorithms are used in this purpose and a regularized classification algorithm is shown to produce good behavior on real data.Lire moins >
Lire la suite >Embodied Conversational Agents (ECAs) are emerging as a key component to allow human interact with machines. Applications are numerous and ECAs can reduce the aversion to interact with a machine by providing user-friendly interfaces. Yet, ECAs are still unable to produce social signals appropriately during their interaction with humans, which tends to make the interaction less instinctive. Especially, very little attention has been paid to the use of laughter in human-avatar interactions despite the crucial role played by laughter in human-human interaction. In this paper, methods for predicting when and how to laugh during an interaction for an ECA are proposed. Different Imitation Learning (also known as Apprenticeship Learning) algorithms are used in this purpose and a regularized classification algorithm is shown to produce good behavior on real data.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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