Brian Ravenet

From a User-created Corpus of Virtual Agent’s Non-verbal Behavior to a Computational Model of Interpersonal Attitudes

Human’s non-verbal behavior may convey different meanings. They can reflect one’s emotional states, communicative intentions but also his social relations with someone else, i.e. his interpersonal attitude. In order to determine the non-verbal behavior that a virtual agent should display to convey particular interpersonal attitudes, we have collected a corpus of virtual agent’s non-verbal behavior directly created by users. Based on the analysis of the corpus, we propose a Bayesian model to automatically compute the virtual agent’s non-verbal behavior conveying interpersonal attitudes.

Conversational Behavior Reflecting Interpersonal Attitudes in Small Group Interactions

In this paper we propose a computational model for the real time generation of nonverbal behaviors supporting the expression of interpersonal attitudes for turn-taking strategies and group formation in multi-party conversations among embodied conversational agents. Starting from the desired attitudes that an agent aims to express towards every other participant, our model produces the nonverbal behavior that should be exhibited in real time to convey such attitudes while managing the group formation and attempting to accomplish the agent’s own turn-taking strategy. We also propose an evaluation protocol for similar multi-agent configurations. We conducted a study following this protocol to evaluate our model. Results showed that subjects properly recognized the attitudes expressed by the agents through their nonverbal behavior and turn taking strategies generated by our system.

Autonomous agents and avatars in REVERIE's virtual environment

In this paper, we describe the enactment of autonomous agents and avatars in the web-based social collaborative virtual environment of REVERIE that supports natural, human-like behavior, physical interaction and engagement. Represented by avatars, users feel immersed in this virtual world in which they can meet and share experiences as in real life. Like the avatars, autonomous agents that may act in this world are capable of demonstrating human-like non-verbal behavior and facilitate social interaction. We describe how reasoning components of the REVERIE system connect and cooperatively control autonomous agents and avatars representing a user.

Interpersonal Attitude of a Speaking Agent in Simulated Group Conversations

Embodied Conversational Agents have been widely used to simulate dyadic interactions with users. We want to explore the context of expression of interpersonal attitudes in simulated group conversations. We are presenting a model that allows agents to exhibit a variety of nonverbal behaviors (e.g gestures, facial expressions, proxemics) depending on the interpersonal attitudes that they want to express within a group while talking. The model combines corpus-based and theoretical-based approaches and we present a preliminary implementation of this model.

A Framework for Human-like Behavior in an immersive virtual world

Just as readers feel immersed when the story line adheres to their experiences, users will more easily feel immersed in a virtual environment if the behavior of the characters in that environment adheres to their expectations, based on their lifelong observations in the real world. This paper introduces a framework that allows authors to establish natural, human-like behavior, physical interaction and emotional engagement of characters living in a virtual environment. Represented by realistic virtual characters, this framework allows people to feel immersed in an Internet based virtual world in which they can meet and share experiences in a natural way as they can meet and share experiences in real life. Rather than just being visualized in a 3D space, the virtual characters (autonomous agents as well as avatars representing users) in the immersive environment facilitate social interaction and multi-party collaboration, mixing virtual with real.

Automating the Production of Communicative Gestures in Embodied Characters

In this paper we highlight the different challenges in modeling communicative gestures for Embodied Conversational Agents (ECAs). We describe models whose aim is to capture and understand the specific characteristics of communicative gestures in order to envision how an automatic communicative gesture production mechanism could be built. The work is inspired by research on how human gesture characteristics (e.g., shape of the hand, movement, orientation and timing with respect to the speech) convey meaning. We present approaches to computing where to place a gesture, which shape the gesture takes and how gesture shapes evolve through time. We focus on a particular model based on theoretical frameworks on metaphors and embodied cognition that argue that people can represent, reason about and convey abstract concepts using physical representations and processes, which can be conveyed through physical gestures.

Architecture of a socio-conversational agent in virtual worlds

Virtual worlds are more and more populated with autonomous conversational agents embodying different roles like tutor, guide, or personal assistant. In order to create more engaging and natural interactions, these agents should be endowed with social capabilities such as expressing different social attitudes through their behaviors. In this paper, we present the architecture of a socio-conversational agent composed of communicative components to detect and respond verbally and non-verbally to the users speech and to convey different social attitudes. This paper presents the main components of this architecture. These descrpitions are illustrated with scenarios of interaction.