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Enhancing Social Believability of Virtual Agents using Social Power Dynamics

Conference paper
M Hashemian, R Prada, PA Santos, S Mascarenhas

FIDES: How Emotions and Small Talks May Influence Trust in an Embodied vs. Non-embodied Robot

Conference paper
R Paradeda, M Hashemian, C Guerra, R Prada, J Dias, A Paiva

How facial expressions and small talk may influence trust in a robot

RB Paradeda, M Hashemian, RA Rodrigues, A Paiva

“How is his/her mood”: A Question that a Companion Robot may be able to answer

M Hashemian, H Moradi, MS Mirian

Is the Mood Really in the Eye of the Beholder?

M Hashemian, H Moradi, MS Mirian, M Tehrani-Doost, RK Ward

Determining mood via emotions observed in face by induction

Mojgan Hashemian, Hadi Moradi, Maryam S Mirian, Mehdi Tehrani-doost

Determining Mood Using Emotional Features

Conference paper
Mojgan Hashemian, Amin Nikoukaran, Hadi Moradi, Maryam Mirian, Mehdi Tehrani-doost
7th IEEE International Symposium on Telecommunications (pp. 418-423)

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Abstract

The ability to determine mood is one of fundamental challenges in affective computing. In this paper, we present a novel approach for mood detection via emotional variations. In this approach, the mood is considered as a low magnitude and more stable, i.e. low frequency, emotion that can be detected using emotion detection approaches. A Bayes classification is applied on a feature vector composed of statistical aspects of the intensity of the emotions. The approach has been implemented in which two emotions, i.e. happiness and sadness, and also neutral state, have been targeted to determine the good, bad, and neutral, mood of subjects respectively. A Bayes classification is applied on a feature vector containing statistical aspects of the intensity of the emotions. The obtained Correct Classification Rate (CCR) is 91.1, with 0.09 mean error and variance of 4.9 discriminating good mood vs. neutral.

Persuasive Social Robots using Social Power Dynamics

Conference paper
Mojgan Hashemian
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems

Social Power, the potential for social influence, is a pervasive social process in human interactions. On the other hand, recent advances on Social Robotics raise the question whether a social robot can be used as a persuasive agent. To date, different attempts have been performed using several approaches to tackle this research question. However, few studies looked at the concept of social power in Human-Robot Interaction (HRI) and how it can be beneficial to the development of persuasion skills. This is the precisely the goal of the work that is described here. In this text, we briefly report the results of our recent advancements for this objective and draw suggestion for speculating on future directions.

Social Power in Human-Robot Interaction: Towards More Persuasive Robots

Mojgan Hashemian, Ana Paiva, Samuel Mascarenhas, Pedro A. Santos, Rui Prada
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems

Social power is defined as one’s ability to influence another to do something which s/he would not do without the presence of such power. Different theories classify alternative ways to achieve social power, such as providing a reward, using coercion, or acting as an expert. In this work, we explored two types of persuasive strategies that are based on social power (specifically Reward and Expertise) and created two social robots that would employ such strategies. To examine the effectiveness of these strategies we performed a user study with 51 participants using two social robots in an adversarial setting in which both robots try to persuade the user on a concrete choice. The results show that even though each of the strategies caused the robots to be perceived differently in terms of their competence and warmth, both were similarly persuasive.