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Related Experiment Video

Updated: May 28, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Smiling virtual agent in social context.

Magalie Ochs¹, Radoslaw Niewiadomski, Paul Brunet

¹CNRS-LTCI, Télécom ParisTech, Paris, France. ochs@telecom-paristech.fr

Cognitive Processing

|October 13, 2011

Summary

This summary is machine-generated.

This study introduces an algorithm to differentiate virtual agent smiles, distinguishing between amusement, politeness, and embarrassment based on facial expression characteristics. The algorithm was validated through user-generated smile data and decision tree analysis.

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Published on: March 1, 2017

Area of Science:

Computer Science
Human-Computer Interaction
Artificial Intelligence

Background:

Facial expressions, particularly smiles, convey diverse communicative intentions.
Subtle variations in smile morphology and dynamics can signal different emotions or social cues.
Accurately interpreting virtual agent smiles is crucial for natural human-computer interaction.

Purpose of the Study:

To propose and validate an algorithm for classifying virtual agent smiles.
To differentiate between smiles of amusement, politeness, and embarrassment.
To analyze the morphological and dynamic characteristics of these distinct smile types.

Main Methods:

Development of a virtual agent smile corpus based on user-generated data.
Application of decision tree classification techniques for smile analysis.
Evaluation of the algorithm's performance in various interactive contexts.

Main Results:

The proposed algorithm successfully identifies and categorizes virtual agent smiles.
Morphological and dynamic features were found to be key discriminators for amusement, politeness, and embarrassment smiles.
Validation across different contexts confirmed the algorithm's effectiveness.

Conclusions:

The algorithm provides a robust method for interpreting nuanced virtual agent facial expressions.
This contributes to more realistic and emotionally intelligent virtual agents.
Understanding smile variations enhances the effectiveness of human-computer communication.