Emotional Context? Or Contextual Emotions?

The question “What is the meaning of a smile?” could be easily answered with the sentence “it means happiness”. But we can see in our daily lives that it is not always true. We also recognize that there is the context the one that makes us differentiate a happy smile from an embarrassed smile. The context is the framework that gives emotions a reason for happening because it describes what occurs around a person. Therefore, to create virtual characters, or agents that express emotions in a believable way it is necessary to simulate the context around them. The novelty of this chapter is the representation of context using ontologies, where context is seen not only as the events in the world, but also as that part of the character which allows them to react in one way or another, resulting in more believable emotional responses.

EEG-Analysis for the Detection of True Emotion or Pretension

Several lobes in the human brain are involved differently in the arousal, processing and manifestation of emotion in facial expression, vocal intonation and gestural patterns. Sometimes people suppress their bodily manifestations to pretend their emotions. Detection of emotion and pretension is an open problem in emotion research. The chapter presents an analysis of EEG signals to detect true emotion/pretension: first by extracting the neural connectivity among selected brain lobes during arousal and manifestation of a true emotion, and then by testing whether the connectivity among the lobes are maintained while encountering an emotional context. In case the connectivity is manifested, the arousal of emotion is regarded as true emotion, otherwise it is considered as a pretension. Experimental results confirm that for positive emotions, the decoding accuracy of true (false) emotions is as high as 88% (72%), while for negative emotions, the classification accuracy falls off by a 12% margin for true emotions and 8% margin for false emotions. The proposed method has wide-spread applications to detect criminals, frauds and anti-socials.

Intention and Body-Mood Engineering via Proactive Robot Moves in HRI

This chapter focuses on emotion and intention engineering by socially interacting robots that induce desired emotions/intentions in humans. The authors provide all phases that pave this road, supported by overviews of leading works in the literature. The chapter is partitioned into intention estimation, human body-mood detection through external-focused attention, path planning through mood induction and reshaping intention. Moreover, the authors present their novel concept, with implementation, of reshaping current human intention into a desired one, using contextual motions of mobile robots. Current human intention has to be deviated towards the new desired one by destabilizing the obstinance of human intention, inducing positive mood and making the “robot gain curiosity of human”. Deviations are generated as sequences of transient intentions tracing intention trajectories. The authors use elastic networks to generate, in two modes of body mood: “confident” and “suspicious”, transient intentions directed towards the desired one, choosing among intentional robot moves previously learned by HMM.

Socially Embodied Human-Robot Interaction

A fundamental challenge of human interaction with socially interactive robots, compared to other interactive products, comes from them being embodied. The embodied nature of social robots questions to what degree humans can interact ‘naturally' with robots, and what impact the interaction quality has on the user experience (UX). UX is fundamentally about emotions that arise and form in humans through the use of technology in a particular situation. This chapter aims to contribute to the field of human-robot interaction (HRI) by addressing, in further detail, the role and relevance of embodied cognition for human social interaction, and consequently what role embodiment can play in HRI, especially for socially interactive robots. Furthermore, some challenges for socially embodied interaction between humans and socially interactive robots are outlined and possible directions for future research are presented. It is concluded that the body is of crucial importance in understanding emotion and cognition in general, and, in particular, for a positive user experience to emerge when interacting with socially interactive robots.

The Exploitation of Models in Artificial Emotions

Cognitive processes might be seen as reciprocal items and they are usually characterized by multiple feedback cycles. Emotions constitute one major source of feedback loops to assure the maintenance of well-being, providing cognitive processes with quantifiable meaning. This suggests the exploitation of models to improve the adaptation under value-based protocols. Emotion is not an isolated effect of stimuli, but it is the set of several effects of the stimuli and the relationships among them. This chapter proposes a study of the exploitation of models in artificial emotions, pointing out relationships as part of the model as well as the model exploitation method.

On Realizing Emotional Memories

Emotion and memory have been two intermingled areas in psychological research. Although researchers are still fairly clueless on how human emotions or memory work, several attempts have been made to copy the dynamics of these two entities in the realm of robotics. This chapter describes one such attempt to capture the dynamics of human emotional memories and model the same for use in a real robot. Emotional memories are created at extreme emotional states, namely, very positive or happy events or very negative ones. The positive ones result in the formation of positive memories while the negative ones form the negative counterparts. The robotic system seeks the positive ones while it tries to avoid the negative ones. Such memories aid the system in making the right decisions, especially when situations similar to the one which caused their generation, repeat in the future. This chapter introduces the manner in which a multi-agent emotion engine churns out the emotions which in turn generate emotional memories. Results obtained from simulations and those from using a real situated robot described herein, validate the working of these memories.

Could Emotions Be Modelled through Information Processing?

One way to evaluate cognitive processes in living or nonliving systems is by using the notion of “information processing”. Emotions as cognitive processes orient human beings to recognize, express and display themselves or their wellbeing through dynamical and adaptive form of information processing. In addition, humans behave or act emotionally in an embodied environment. The brain embeds symbols, meaning and purposes for emotions as well. So any model of natural or autonomous emotional agents/systems needs to consider the embodied features of emotions that are processed in an informational channel of the brain or a processing system. This analytical and explanatory study described in this chapter uses the pragmatic notion of information to develop a theoretical model for emotions that attempts to synthesize some essential aspects of human emotional processing. The model holds context-sensitive and purpose-based features of emotional pattering in the brain. The role of memory is discussed and an idea of control parameters that have roles in processing environmental variables in emotional patterning is introduced.

Computational Analytical Framework for Affective Modeling

Computational affective models are being developed both to elucidate affective mechanisms, and to enhance believability of synthetic agents and robots. Yet in spite of the rapid growth of computational affective modeling, no systematic guidelines exist for model design and analysis. Lack of systematic guidelines contributes to ad hoc design practices, hinders model sharing and re-use, and makes systematic comparison of existing models and theories challenging. Lack of a common computational terminology also hinders cross-disciplinary communication that is essential to advance our understanding of emotions. In this chapter the author proposes a computational analytical framework to provide a basis for systematizing affective model design by: (1) viewing emotion models in terms of two core types: emotion generation and emotion effects, and (2) identifying the generic computational tasks necessary to implement these processes. The chapter then discusses how these computational ‘building blocks' can support the development of design guidelines, and a systematic analysis of distinct emotion theories and alternative means of their implementation.

Military Robotics and Emotion

In this chapter the author considers the complex moral interplay between unmanned systems, emotion, and just war theory. The first section examines technologically mediated fighting and suggests that through a process of moral-emotional disengagement and emotional desensitisation, any pre-existing barriers to immoral conduct in war may be reduced. Having considered the impact on the long distance warrior's capacity or willingness to adhere to jus in bello norms, the author then examines the impact on the personal wellbeing of the operators themselves. Here, among other things, the author considers the impact of being simultaneously present in contrasting environments and argue that this, if nothing else, may lead to serious transgressions of just war principles. The fourth and final section asks whether we can eliminate or relieve some of these technologically mediated but distinctly human moral problems by further automating elements of the decision making process.

Developing Robot Emotions through Interaction with Caregivers

In this chapter, the authors explore social constructivist theories of emotion, which suggest that emotional behaviors are developed through experience, rather than innate. The authors' approach to artificial emotions follows this paradigm, stemming from a relatively young field called developmental or ‘epigenetic' robotics. The chapter describes the design and implementation of a robot called MEI (multimodal emotional intelligence) with an emotion development system. MEI synchronizes to humans through voice and movement dynamics, based on mirror mechanism-like entrainment. Via typical caregiver interactions, MEI associates these dynamics with its physical feeling, e.g. distress (low battery or excessive motor heat) or flourishing (homeostasis). Our experimental results show that emotion clusters developed through robot-directed motherese (“baby talk”) are similar to adult happiness and sadness, giving evidence to constructivist theories.