A virtual diary companion

Chatbots and embodied conversational agents show turn based conversation behaviour. In current research we almost always assume that each utterance of a human conversational partner should be followed by an intelligent and/or empathetic reaction of chatbot or embodied agent. They are assumed to be alert, trying to please the user. There are other applications which have not yet received much attention and which require a more patient or relaxed attitude, waiting for the right moment to provide feedback to the human partner. Being able and willing to listen is one of the conditions for being successful. In this paper we have some observations on listening behaviour research and introduce one of our applications, the virtual diary companion

Interaction with a Virtual Coach for Active and Healthy Ageing

International audienceSince life expectancy has increased significantly over the past century, society is being forced to discover innovative ways to support active aging and elderly care. The e-VITA project, which receives funding from both the European Union and Japan, is built on a cutting edge method of virtual coaching that focuses on the key areas of active and healthy aging. The requirements for the virtual coach were ascertained through a process of participatory design in workshops, focus groups, and living laboratories in Germany, France, Italy, and Japan. Several use cases were then chosen for development utilising the open-source Rasa framework. The system uses common representations such as Knowledge Bases and Knowledge Graphs to enable the integration of context, subject expertise, and multimodal data, and is available in English, German, French, Italian, and Japanese

Affective reactions towards socially interactive agents and their computational modeling

Over the past 30 years, researchers have studied human reactions towards machines applying the Computers Are Social Actors paradigm, which contrasts reactions towards computers with reactions towards humans. The last 30 years have also seen improvements in technology that have led to tremendous changes in computer interfaces and the development of Socially Interactive Agents. This raises the question of how humans react to Socially Interactive Agents. To answer these questions, knowledge from several disciplines is required, which is why this interdisciplinary dissertation is positioned within psychology and computer science. It aims to investigate affective reactions to Socially Interactive Agents and how these can be modeled computationally. Therefore, after a general introduction and background, this thesis first provides an overview of the Socially Interactive Agent system used in this work. Second, it presents a study comparing a human and a virtual job interviewer, which shows that both interviewers induce shame in participants to the same extent. Thirdly, it reports on a study investigating obedience towards Socially Interactive Agents. The results indicate that participants obey human and virtual instructors in similar ways. Furthermore, both types of instructors evoke feelings of stress and shame to the same extent. Fourth, a stress management training using biofeedback with a Socially Interactive Agent is presented. The study shows that a virtual trainer can teach coping techniques for emotionally challenging social situations. Fifth, it introduces MARSSI, a computational model of user affect. The evaluation of the model shows that it is possible to relate sequences of social signals to affective reactions, taking into account emotion regulation processes. Finally, the Deep method is proposed as a starting point for deeper computational modeling of internal emotions. The method combines social signals, verbalized introspection information, context information, and theory-driven knowledge. An exemplary application to the emotion shame and a schematic dynamic Bayesian network for its modeling are illustrated. Overall, this thesis provides evidence that human reactions towards Socially Interactive Agents are very similar to those towards humans, and that it is possible to model these reactions computationally.In den letzten 30 Jahren haben Forschende menschliche Reaktionen auf Maschinen untersucht und dabei das “Computer sind soziale Akteure”-Paradigma genutzt, in dem Reaktionen auf Computer mit denen auf Menschen verglichen werden. In den letzten 30 Jahren hat sich ebenfalls die Technologie weiterentwickelt, was zu einer enormen Veränderung der Computerschnittstellen und der Entwicklung von sozial interaktiven Agenten geführt hat. Dies wirft Fragen zu menschlichen Reaktionen auf sozial interaktive Agenten auf. Um diese Fragen zu beantworten, ist Wissen aus mehreren Disziplinen erforderlich, weshalb diese interdisziplinäre Dissertation innerhalb der Psychologie und Informatik angesiedelt ist. Sie zielt darauf ab, affektive Reaktionen auf sozial interaktive Agenten zu untersuchen und zu erforschen, wie diese computational modelliert werden können. Nach einer allgemeinen Einführung in das Thema gibt diese Arbeit daher, erstens, einen Überblick über das Agentensystem, das in der Arbeit verwendet wird. Zweitens wird eine Studie vorgestellt, in der eine menschliche und eine virtuelle Jobinterviewerin miteinander verglichen werden, wobei sich zeigt, dass beide Interviewerinnen bei den Versuchsteilnehmenden Schamgefühle in gleichem Maße auslösen. Drittens wird eine Studie berichtet, in der Gehorsam gegenüber sozial interaktiven Agenten untersucht wird. Die Ergebnisse deuten darauf hin, dass Versuchsteilnehmende sowohl menschlichen als auch virtuellen Anleiterinnen ähnlich gehorchen. Darüber hinaus werden durch beide Instruktorinnen gleiche Maße von Stress und Scham hervorgerufen. Viertens wird ein Biofeedback-Stressmanagementtraining mit einer sozial interaktiven Agentin vorgestellt. Die Studie zeigt, dass die virtuelle Trainerin Techniken zur Bewältigung von emotional herausfordernden sozialen Situationen vermitteln kann. Fünftens wird MARSSI, ein computergestütztes Modell des Nutzeraffekts, vorgestellt. Die Evaluation des Modells zeigt, dass es möglich ist, Sequenzen von sozialen Signalen mit affektiven Reaktionen unter Berücksichtigung von Emotionsregulationsprozessen in Beziehung zu setzen. Als letztes wird die Deep-Methode als Ausgangspunkt für eine tiefer gehende computergestützte Modellierung von internen Emotionen vorgestellt. Die Methode kombiniert soziale Signale, verbalisierte Introspektion, Kontextinformationen und theoriegeleitetes Wissen. Eine beispielhafte Anwendung auf die Emotion Scham und ein schematisches dynamisches Bayes’sches Netz zu deren Modellierung werden dargestellt. Insgesamt liefert diese Arbeit Hinweise darauf, dass menschliche Reaktionen auf sozial interaktive Agenten den Reaktionen auf Menschen sehr ähnlich sind und dass es möglich ist diese menschlichen Reaktion computational zu modellieren.Deutsche Forschungsgesellschaf

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

Job crafting and organizational commitment: The mediating role of person-job fit in the food and beverage sector

Purpose – Job crafting plays an essential role in job design since it is a new approach from the employee’s perspective to the manager. However, little research has investigated the hotel industry’s job crafting, particularly in the food and beverage department. Therefore, this study examines the relationship between job crafting and organizational commitment through the mediating role of person-job fit in the food and beverage department in five-star hotels. Design/methodology/approach – This study presents a conceptual framework with eight constructs. In this study, a self-administered questionnaire was used to collect data from full-time food and beverage employees in a five-star hotel. The proposed model was examined using confirmatory factor analysis, reliability analysis, validity analysis, standardized path coefficients, and the Sobel test for mediation analysis. Findings – According to the findings of this study, job crafting factors impact the person-job fit of food and beverage employees. The results also demonstrate that job crafting, and organizational commitment are mediated by person-job fit. The originality of the research – This paper adds a new chain to job design practices in the hospitality industry (i.e., job crafting leads to person-job fit, which enhances employees’ organizational commitment). Practical implications – Employee job crafting impacts the hotel by improving the fit of employees to their jobs, which leads to higher organizational commitment. As a result, hotel job designers should update their job design methodologies to include job crafting practices for hospitality employees

A transparent framework towards the context-sensitive recognition of conversational engagement

Modelling and recognising affective and mental user states is an urging topic in multiple research fields. This work suggests an approach towards adequate recognition of such states by combining state-of-the-art behaviour recognition classifiers in a transparent and explainable modelling framework that also allows to consider contextual aspects in the inference process. More precisely, in this paper we exemplify the idea of our framework with the recognition of conversational engagement in bi-directional conversations. We introduce a multi-modal annotation scheme for conversational engagement. We further introduce our hybrid approach that combines the accuracy of state-of-the art machine learning techniques, such as deep learning, with the capabilities of Bayesian Networks that are inherently interpretable and feature an important aspect that modern approaches are lacking - causal inference. In an evaluation on a large multi-modal corpus of bi-directional conversations, we show that this hybrid approach can even outperform state-of-the-art black-box approaches by considering context information and causal relations

Coaching Imagery to Athletes with Aphantasia

We administered the Plymouth Sensory Imagery Questionnaire (Psi-Q) which tests multi-sensory imagery, to athletes (n=329) from 9 different sports to locate poor/aphantasic (baseline scores <4.2/10) imagers with the aim to subsequently enhance imagery ability. The low imagery sample (n=27) were randomly split into two groups who received the intervention: Functional Imagery Training (FIT), either immediately, or delayed by one month at which point the delayed group were tested again on the Psi-Q. All participants were tested after FIT delivery and six months post intervention. The delayed group showed no significant change between baseline and the start of FIT delivery but both groups imagery score improved significantly (p=0.001) after the intervention which was maintained six months post intervention. This indicates that imagery can be trained, with those who identify as having aphantasia (although one participant did not improve on visual scores), and improvements maintained in poor imagers. Follow up interviews (n=22) on sporting application revealed that the majority now use imagery daily on process goals. Recommendations are given for ways to assess and train imagery in an applied sport setting

Socially Believable Robots

Long-term companionship, emotional attachment and realistic interaction with robots have always been the ultimate sign of technological advancement projected by sci-fi literature and entertainment industry. With the advent of artificial intelligence, we have indeed stepped into an era of socially believable robots or humanoids. Affective computing has enabled the deployment of emotional or social robots to a certain level in social settings like informatics, customer services and health care. Nevertheless, social believability of a robot is communicated through its physical embodiment and natural expressiveness. With each passing year, innovations in chemical and mechanical engineering have facilitated life-like embodiments of robotics; however, still much work is required for developing a “social intelligence” in a robot in order to maintain the illusion of dealing with a real human being. This chapter is a collection of research studies on the modeling of complex autonomous systems. It will further shed light on how different social settings require different levels of social intelligence and what are the implications of integrating a socially and emotionally believable machine in a society driven by behaviors and actions