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International audienc

A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations

One potential solution to help people change their eating behavior is to develop conversational systems able to recommend healthy recipes. Beyond the intrinsic quality of the recommendations themselves, various factors might also influence users? perception of a recommendation. Two of these factors are the conversational skills of the system and users' interaction modality. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users? eating habits and current preferences. Users can interact with Cora in two different ways. They can select predefined answers by clicking on buttons to talk to Cora or write text in natural language. On the other hand, Cora can engage users through a social dialogue, or go straight to the point. We conduct an experiment to evaluate the impact of Cora's conversational skills and users' interaction mode on users' perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception of the system

A Model of Social Explanations for a Conversational Movie Recommendation System

A critical aspect of any recommendation process is explaining the reasoning behind each recommendation. These explanations can not only improve users' experiences, but also change their perception of the recommendation quality. This work describes our human-centered design for our conversational movie recommendation agent, which explains its decisions as humans would. After exploring and analyzing a corpus of dyadic interactions, we developed a computational model of explanations. We then incorporated this model in the architecture of a conversational agent and evaluated the resulting system via a user experiment. Our results show that social explanations can improve the perceived quality of both the system and the interaction, regardless of the intrinsic quality of the recommendations

An End-to-End Conversational Style Matching Agent

We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation

Vers des Agents Conversationnels Animés dotés d'émotions et d'attitudes sociales

International audienceIn this article, we propose an architecture of a socio-affective Embodied Conversational Agent (ECA). The different computational models of the architecture enable an ECA to express emotions and social attitudes during an interaction with a user. Based on corpora of actors expressing emotions, models have been defined to compute the emotional facial expressions of an ECA and the characteristics of its corporal movements. A user-perceptive approach has been used to design models to define how an ECA should adapt its non-verbal behavior according to the social attitude the ECA wants to display and the behavior of its interlocutor. The emotions and the social attitudes to express are computed by cognitive models presented in this article.Dans cet article, nous proposons une architecture d'un Agent Conversationnel Animé (ACA) socio-affectif. Les différents modèles computationnels sous-jacents à cette architecture, permettant de donner la capacité à un ACA d'exprimer des émotions et des attitudes sociales durant son interaction avec l'utilisateur, sont présentés. A partir de corpus d'individus exprimant des émotions, des modèles permettant de calculer l'expression faciale émotionnelle d'un ACA ainsi que les caractéristiques de ses mouvements du corps ont été définis. Fondés sur une approche centrée sur la perception de l'utilisateur, des modèles permettant de calculer comment un ACA doit adapter son comportement non-verbal suivant l'attitude sociale qu'il souhaite exprimer et suivant le comportement de son interlocuteur ont été construits. Le calcul des émotions et des attitudes sociales à exprimer est réalisé par des modèles cognitifs présentés dans cet article

Affective interaction with a virtual character through an fNIRS brain-computer interface

Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent’s facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent’s responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment

Modélisation de la prise de décision d'un agent conversationnel animé en fonction de son attitude sociale

To be perceived as believable partners in human-machine interactions, virtual agents have to express adequate social attitudes. The social attitude expressed by an agent should reflect the social situation of the interaction. The agent ought to take into account its role and its social relation toward its interactants when deciding how to react in the interaction. To build such an agent able to reason about its role and relation and to adapt its social attitude, we built a model of social decision making. First, we formalized the dynamics of the social relation through a combination of goals and beliefs. Then, we designed a decision making model based on the social goals and the situational goals of the agent. Finally, we conducted an empirical study in the context of virtual tutor-child interaction where participants evaluated the tutor’s perceived social attitude towards the child while the tutor’s social role and relation were manipulated by our model. Results showed that both role and social relation have an influence on the agent’s perceived social attitude.Afin d’être considérés comme des partenaires crédibles lors d’une interaction, les agents virtuels doivent transmettre une attitude sociale adéquate. Cette attitude sociale exprimée par l’agent doit refléter la situation dans laquelle il se trouve. L’agent doit donc prendre en compte son rôle et sa relation sociale vis à vis de son interlocuteur lorsqu’il choisit comment réagir au cours de l’interaction. Afin de construire un tel agent capable de raisonner en fonction de son rôle et de sa relation, et capable d’adapter son attitude sociale, nous avons construit un modèle de prise de décision sociale. Dans un premier temps, nous formalisons la dynamique de la relation sociale à travers une combinaison de buts et de croyances. Puis, nous définissons un modèle de prise de décision basé sur les buts sociaux et situationnels de l’agent. Pour finir, nous avons réalisé une étude perceptive dans un contexte d’interaction tuteur/enfant virtuels au cours de laquelle les participants évaluaient l’attitude sociale du tuteur envers l’enfant. La relation sociale et le rôle social du tuteur étaient manipulés par notre modèle. Les résultats montrent qu’à la fois le rôle et la relation du tuteur ont une influence sur son attitude sociale perçue