Artificial Emotions for Distributed Cyber-physical Systems Resilience

International audienceThe concept of system resilience is important and popular in different domains like psychology, psychiatry, sociology, and more recently in cognitive science, biological disciplines, ecology and computer science. The main objective of this paper is to present a research avenue exploring the applicability of knowledge from those domains to solve resilience problems in cyber-physical systems. Emotions have been identified as an important process to cope with unexpected events and is therefore crucial for resilience. Our work is thus aimed at utilizing emotion-like processes in cyber-physical systems to improve their resilience, at individual and collective levels. Furthermore, one of our main assumptions is that the multi-agent paradigm is particularly well suited to embed such emotion-like processes in this type of systems

Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges

Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation's recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling Conference, Canberra, Australi

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

Original article can be found at: http://www.igi-global.com/articles/details.asp?ID=35052 Copyright IGI. Posted by permission of the publisher.This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modeling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottomup approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.Peer reviewe

Exploring passive user interaction for adaptive narratives

Previous Interactive Storytelling systems have been designed to allow active user intervention in an unfolding story, using established multi-modal interactive techniques to influence narrative development. In this paper we instead explore the use of a form of passive interaction where users' affective responses, measured by physiological proxies, drive a process of narrative adaptation. We introduce a system that implements a passive interaction loop as part of narrative generation, monitoring users' physiological responses to an on-going narrative visualization and using these to adapt the subsequent development of character relationships, narrative focus and pacing. Idiomatic cinematographic techniques applied to the visualization utilize existing theories of establishing characteristic emotional tone and viewer expectations to foster additional user response. Experimental results support the applicability of filmic emotional theories in a non-film visual realization, demonstrating significant appropriate user physiological response to narrative events and "emotional cues". The subsequent narrative adaptation provides a variation of viewing experience with no loss of narrative comprehension

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

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