Environnements virtuels émotionnellement intelligents

Les émotions ont été étudiées sous différents angles dans le domaine de l'interaction homme-machine y compris les systèmes tutoriel intelligents, les réseaux sociaux, les plateformes d’apprentissage en ligne et le e-commerce. Beaucoup d’efforts en informatique affective sont investis pour intégrer la dimension émotionnelle dans les environnements virtuels (tel que les jeux vidéo, les jeux sérieux et les environnements de réalité virtuelle ou de réalité augmenté). Toutefois, les stratégies utilisées dans les jeux sont encore empiriques et se basent sur des modèles psychologiques et sociologiques du joueur : Courbe d’apprentissage, gestion de la difficulté, degré d’efficience dans l’évaluation des performances et de la motivation du joueur. Or cette analyse peut malmener le système dans la mesure où les critères sont parfois trop vagues ou ne représentent pas les réelles compétences du joueur, ni ses vraies difficultés. Étant donné que la stratégie d’intervention est très influencée par la précision de l’analyse et l’évaluation du joueur, de nouveaux moyens sont nécessaires afin d’améliorer les processus décisionnels dans les jeux et d’organiser les stratégies d’adaptation de façon optimale. Ce travail de recherche vise à construire une nouvelle approche pour l’évaluation et le suivi du joueur. L’approche permet une modélisation du joueur plus efficace et moins intrusive par l’intégration des états mentaux et affectifs obtenus à partir de senseurs physiologiques (signaux cérébraux, Activité électrodermale, …) ou/et instruments optiques (Webcam, traceur de regard, …). Les états affectifs et mentaux tels que les émotions de base (basées sur les expressions faciales), l’état d’engagement, de motivation et d’attention sont les plus visés dans cette recherche. Afin de soutenir l’adaptation dans les jeux, des modèles des émotions et de la motivation du joueur basé sur ces indicateurs mentaux et affectifs, ont été développés. Nous avons implémenté cette approche en développant un système sous forme d’une architecture modulaire qui permet l’adaptation dans les environnements virtuels selon les paramètres affectifs du joueur détectés en temps-réel par des techniques d’intelligence artificielle.Emotions were studied from different angles in the field of human-machine interaction including intelligent tutorial systems, social networks, online learning platforms and e-commerce. Much effort in affective computing are invested to integrate the emotional dimension in virtual environments (such as video games, serious games and virtual reality environments or augmented reality). However, the strategies used in games are still empirical and are based on psychological and sociological models of the player: Learning Curve, trouble management, degree of efficiency in the evaluation of performance and motivation of the player. But this analysis can mislead the system to the extent that the criteria are sometimes too vague and do not represent the actual skills of the player, nor his real difficulties. Since the intervention strategy is influenced by the accuracy of the analysis and evaluation of the player, new ways are needed to improve decision-making in games and organizing adaptation strategies in optimal way. This research aims to build a new approach to the evaluation and monitoring of the player. The approach enables more effective and less intrusive player modeling through the integration of mental and emotional states obtained from physiological sensors (brain signals, electro-dermal activity, ...) or/and optical instruments (Webcam, eye-tracker, ...). The emotional and mental states such as basic emotions (based on facial expressions), the states of engagement, motivation and attention are the most targeted in this research. In order to support adaptation in games, models of emotions and motivation of the player based on these mental and emotional indicators, have been developed. We have implemented this approach by developing a system in the form of a modular architecture that allows adaptation in virtual environments according to the player's emotional parameters detected in real time by artificial intelligence methods

Assessing Persuasion in Argumentation through Emotions and Mental States

International audienc

Assessing Persuasion in Argumentation through Emotions and Mental States

International audienc

Persuasive Argumentation and Emotions: An Empirical Evaluation with Users

International audienc

Emotions in Argumentation: an Empirical Evaluation

International audienceArgumentation is often seen as a mechanism to support different forms of reasoning such that decision-making and persuasion, but all these approaches assume a purely rational behavior of the involved actors. However, humans are proved to behave differently, mixing rational and emotional attitudes to guide their actions, and it has been claimed that there exists a strong connection between the argumentation process and the emotions felt by people involved in such process. In this paper , we assess this claim by means of an experiment: during several debates people's argumenta-tion in plain English is connected and compared to the emotions automatically detected from the participants. Our results show a correspondence between emotions and argumentation elements, e.g., when in the argumentation two opposite opinions are conflicting this is reflected in a negative way on the debaters' emotions

Persuasive Argumentation and Emotions: An Empirical Evaluation with Users

International audienc

Emotions and personality traits in argumentation: An empirical evaluation1

International audienceArgumentation is a mechanism to support different forms of reasoning such as decision making and persuasion and always cast under the light of critical thinking. In the latest years, several computational approaches to argumentation have been proposed to detect conflicting information, take the best decision with respect to the available knowledge, and update our own beliefs when new information arrives. The common point of all these approaches is that they assume a purely rational behavior of the involved actors, be them humans or artificial agents. However, this is not the case as humans are proved to behave differently, mixing rational and emotional attitudes to guide their actions. Some works have claimed that there exists a strong connection between the argumentation process and the emotions felt by people involved in such process. We advocate a complementary, descriptive and experimental method, based on the collection of emotional data about the way human reasoners handle emotions during debate interactions. Across different debates, people’s argumentation in plain English is correlated with the emotions automatically detected from the participants, their engagement in the debate, and the mental workload required to debate. Results show several correlations among emotions, engagement and mental workload with respect to the argumentation elements. For instance, when two opposite opinions are conflicting, this is reflected in a negative way on the debaters’ emotions. Beside their theoretical value for validating and inspiring computational argumentation theory, these results have applied value for developing artificial agents meant to argue with human users or to assist users in the management of debates