Samuel Mascarenhas

The influence of empathy in human-robot relations

The idea of robotic companions capable of establishing meaningful relationships with humans remains far from being accomplished. To achieve this, robots must interact with people in natural ways, employing social mechanisms that people use while interacting with each other. One such mechanism is empathy, often seen as the basis of social cooperation and prosocial behaviour. We argue that artificial companions capable of behaving in an empathic manner, which involves the capacity to recognise anothers affect and respond appropriately, are more successful at establishing and maintaining a positive relationship with users. This paper presents a study where an autonomous robot with empathic capabilities acts as a social companion to two players in a chess game. The robot reacts to the moves played on the chessboard by displaying several facial expressions and verbal utterances, showing empathic behaviours towards one player and behaving neutrally towards the other. Quantitative and qualitative results of 31 participants indicate that users towards whom the robot behaved empathically perceived the robot as friendlier, which supports our hypothesis that empathy plays a key role in human-robot interaction.

Why can't we be friends? an empathic game companion for long-term interaction

The ability of artificial companions (virtual agents or robots) to establish meaningful relationships with users is still limited. In humans, a key aspect of such ability is empathy, often seen as the basis of social cooperation and pro-social behaviour. In this paper, we present a study where a social robot with empathic capabilities interacts with two users playing a chess game against each other. During the game, the agent behaves in an empathic manner towards one of the players and in a neutral way towards the other. In an experiment conducted with 40 participants, results showed that users to whom the robot was empathic provided higher ratings in terms of companionship.

Modelling empathy in social robotic companions

Empathy can be broadly defined as the ability to understand and respond appropriately to the affective states of others. In this paper, we present a scenario where a social robot acts as a chess companion for children, and describe our current efforts towards endowing such robot with empathic capabilities. A multimodal framework for modeling some of the users affective states that combines visual and task-related features is presented. Using this model of the user, we personalise the learning environment by adapting the robots empathic responses to the particular preferences of the child who is interacting with the robot. We also describe a preliminary study conducted in this scenario.

Social Importance Dynamics: A Model for Culturally-Adaptive Agents

The unwritten rules of human cultures greatly affect social behaviour and as such should be considered in the development of socially intelligent agents. So far, there has been a large focus on modeling cultural aspects related to non-verbal behaviour such as gaze or body posture. However, culture also dictates how we perceive and treat others from a relational perspective. Namely, what do we expect from others in different social situations and how much are we willing to do for others as well. In this article we present a culturally configurable model of such social dynamics. The aim is to facilitate the creation of agents with distinct cultural behaviour, which emerges from different parametrisations of the proposed model. The practical application of the model was tested in the development of an agent-based application for intercultural training, in which the model is responsible for driving the socio-cultural behaviour of the virtual agents.

A DIMENSIONAL MODEL FOR CULTURAL BEHAVIOR IN VIRTUAL AGENTS

Intercultural training is becoming more important than ever before. As a result, there is a stronger need for developing more advanced educational methods. Virtual environments can provide an innovative approach in this area. Users can safely explore complicated intercultural situations by interacting with virtual agents that display human cultural behavior. However, current environments that address cultural aspects focus on explicit aspects such as gestures, spoken language, or norms. Implicit cultural manifestations, such as values, have been greatly disregarded and yet they are a fundamental aspect. This article addresses the integration of such implicit manifestations in an agent architecture, through a dimensional model based on anthropological studies. Using the implemented architecture, two different agent cultures were created. Users were asked to visualize and describe both cultures performing a short emergent story. Results confirmed that users perceived the two cultures with significant differences that were in congruence with the model used.

Closing the loop: from affect recognition to empathic interaction

Empathy is a very important capability in human social relationships. If we aim to build artificial companions (agents or robots) capable of establishing long-term relationships with users, they should be able to understand the users affective state and react accordingly, that is, behave in an empathic manner. Recent advances in affect recognition research show that it is possible to automatically analyse and interpret affective expressions displayed by humans. However, affect recognition in naturalistic environments is still a challenging issue and there are many unanswered questions related to how a virtual agent or a social robot should react to those states, and how that improves the interaction. We have developed a scenario in which a social robot recognises the users affective state and displays empathic behaviours. In this paper, we present part of the results of a study assessing the influence of the robots empathic behaviour on the users understanding of the interaction.

A Process Model of Empathy For Virtual Agents

For more than a century, empathy has been a central topic in the study of human emotion. It plays a crucial role in our everyday social life, having implications for the survival of the species. In the case of agents that inhabit virtual worlds and interact socially among each other and with humans, empathy has also been considered to be an important mechanism to promote engaging and believable interactions. However, creating empathic agents, until recently, has been accomplished mostly through the implementation of specific empathic behaviors or by using domain-dependent empirical models. In this article, we propose a generic computational model of empathy that is grounded on recent psychological theories about empathy. The proposed model treats empathy as a process in which the intensity of the empathic response is modulated by a set of factors that involve the relationship between the agents of the empathic interaction, namely, the similarity and affective link, as well as some characteristics of the empathizer agent, such as mood and personality. This model was implemented into an affective agent architecture, which was then used in an evaluation that had 77 participants. The results indicate that our empathy model, when used to simulate a social scenario with a small group of agents, significantly changed the way that the users perceived and described the interactions between those agents.

Creating a World for Socio-Cultural Agents

Creating agents that are capable of emulating similar socio-cultural dynamics to those found in human interaction remains as one of the hardest challenges of artificial intelligence. This problem becomes particularly important when considering embodied agents that are meant to interact with humans in a believable and empathic manner. In this article, we introduce a conceptual model for socio-cultural agents, and, based on this model, we present a set of requirements for these agents to be capable of showing appropriate socio-cultural behaviour. Our model differentiates between three levels of instantiation: the interaction level, consisting of elements that may change depending on the people involved, the group level, consisting of elements that may change depending on the group affiliation of the people involved, and the society level, consisting of elements that may change depending on the cultural background of those involved. As such, we are able to have culture alter agents’ social relationships rather than directly determining actions, allowing for virtual agents to act more appropriately in any social or cultural context.

Traveller: An Interactive Cultural Training System Controlled by User-Defined Body Gestures

In this paper, we describe a cultural training system based on an interactive storytelling approach and a culturally-adaptive agent architecture, for which a user-defined gesture set was created. 251 full body gestures by 22 users were analyzed to find intuitive gestures for the in-game actions in our system. After the analysis we integrated the gestures in our application using our framework for full body gesture recognition. We further integrated a second interaction type which applies a graphical interface controlled with freehand swiping gestures.

Generating norm-related emotions in virtual agents

The increased believability provided by emotions in virtual characters is a valuable feature in a multi agent environment. Despite much research on how to model emotions based on events that affect a characters goals, the current emotional models usually do not take into account other sources of emotions, such as norms and standards. Moreover, current normative systems usually do not consider the role of emotions. Systems that include emotions and norms are too domain-specific or lack flexibility. We propose a model for the generation of emotions based on the appraisal of actions associated with norm-related events, such as the fulfilment or violation of a norm.