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A compelling body of evidence indicates that observing a task-irrelevant action makes the execution of that action more likely. However, it remains unclear whether this 'automatic imitation' effect is indeed automatic or whether the imitative action is voluntary. The present study tested the automaticity of automatic imitation by asking whether it occurs in a strategic context where it reduces payoffs. Participants were required to play rock-paper-scissors, with the aim of achieving as many wins as possible, while either one or both players were blindfolded. While the frequency of draws in the blind-blind condition was precisely that expected at chance, the frequency of draws in the blind-sighted condition was significantly elevated. Specifically, the execution of either a rock or scissors gesture by the blind player was predictive of an imitative response by the sighted player. That automatic imitation emerges in a context where imitation reduces payoffs accords with its 'automatic' description, and implies that these effects are more akin to involuntary than to voluntary actions. These data represent the first evidence of automatic imitation in a strategic context, and challenge the abstraction from physical aspects of social interaction typical in economic and game theory.

As dialogue agents become increasingly human-like in their performance, we must develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. Here we foreground the concept of role play. Casting dialogue-agent behaviour in terms of role play allows us to draw on familiar folk psychological terms, without ascribing human characteristics to language models that they in fact lack. Two important cases of dialogue-agent behaviour are addressed this way, namely, (apparent) deception and (apparent) self-awareness., (© 2023. Springer Nature Limited.)

During social encounters, people tend to reproduce the facial expressions of others, termed "facial mimicry," which is believed to underlie many important social cognitive functions. Clinically, atypical mimicry is closely associated with serious social dysfunction. However, findings regarding the facial mimicry ability of children with autism spectrum disorder (ASD) are inconsistent; it is necessary to test whether deficits in facial mimicry are core defects of autism and explore the potential mechanism underlying this process. Using quantitative analysis, this study investigated voluntary and automatic facial mimicry performance of six basic expressions in children with and without ASD. There was no significant group difference in mimicry accuracy, but children with ASD showed less intensity in voluntary and automatic mimicry than typically developing children; they also presented less voluntary mimicry intensity for happy, sad, and fearful expressions. Performance on voluntary and automatic mimicry was significantly correlated with the level of autistic symptoms (r >-.43) and theory of mind (r >.34). Furthermore, theory of mind mediated the relationship between autistic symptoms and the intensity of facial mimicry. These results suggest that individuals with ASD show atypical facial mimicry (i.e., less intensity for both voluntary and automatic mimicry, mainly for voluntary mimicry of happiness, sadness, and fear), which might offer a potential cognitive marker for quantifying syndrome manifestations in children with ASD. These findings suggest that theory of mind plays a mediating role in facial mimicry, which may provide insight into the theoretical mechanism of social dysfunction in children with autism., (© 2023 International Society for Autism Research and Wiley Periodicals LLC.)

This paper defines the nature of collective irrationality that flourished during the COVID-19 pandemic and lays out specific individual and shared traits and dispositions that facilitate it. Drawing on the example of globally experienced phenomenon of panicked toilet paper buying and hoarding during the COVID-19 pandemic and resources from philosophy, psychology, sociology, and economics this paper identifies four essential features of collective irrationality: weak shared mentality; non-cognitive and immediate mimicry; affective contagion; and psychosocial adaptivity. After (I) initially pointing out conceptual problems around benchmarking collectivity and irrationality, this paper (II) identifies weak mentality as serving the goals of “group” recognition internally and externally. It is argued that (III) the non-cognitive and immediate mimicry and emotional contagion are shared and individual dispositional conditions that facilitate collective irrationality in environments affected by uncertainty (IV). The human mimetic faculty and susceptibility to emotional contagion are presented as enabling and augmenting conditions under which collective irrationality flourishes. Finally, (IV) presenting collective irrationality in the context of psychosocial adaptivity, the paper provides evolutionary reasons for engaging in irrational behaviors, rendering collective irrationality as an adaptive strategy. [ABSTRACT FROM AUTHOR]

The enduring effects of COVID-19 have called into question many of the assumptions upon which media and cultural studies rest, including a fundamental mode of perception: the sense of smell. In dialog with the field of sensory studies, this paper traces digital smell loss (anosmia) communities from pre-pandemic Facebook groups to mid-pandemic TikTok challenges. This article considers digital smell loss communities on TikTok as imitation publics characterized by repetition. Via replicable TikTok challenges, digital smell-loss communities reckoned with the unmooring effects of a seemingly mild symptom. By exploring how formulaic smell-loss challenges generated support and facilitated community-building, this article demands greater attention to a sense often considered 'disposable'. [ABSTRACT FROM AUTHOR]

The goal of this manuscript is to provide support for the notion that emotional mimicry is a social act. For this, I will provide a brief overview of recent developments in the domain of emotional mimicry research. I will present the mimicry in social context model of mimicry and evidence for four predictions that set this theory apart. Specifically, based on a review of the literature on emotional mimicry, I conclude that we do not mimic the specific muscle movements we observe, but rather we mimic what we infer from these movements. Furthermore, emotional mimicry only occurs when the expresser and observer share the intention to affiliate. Hence, we are less likely to mimic strangers and do not mimic people we do not like. Interactions in which affiliative mimicry occurs are perceived as more positive, but interactions in which mimicry is antagonistic as more negative. This supports the three social functions proposed here: Affiliation, Emotional understanding, and Social regulation., (© 2020 Society for Psychophysiological Research.)

The Swedish Nobel prize winner Harry Martinsson is introduced in this paper, and his epic science fiction poem Aniara is presented. Mima, a miraculous accessor and transmitter of information from all over the universe, is juxtaposed to Sandor Ferenczi's concept of mimicry and Wittgenstein's views on aspect-seeing. It is proposed that the Mima can be seen as a metaphor for the infant mind, having creative mimetic faculties. The single most important realm of mimicry and auto-plastic adaptation is basic trust and empathy. Mimicry is thus suggested to be our deepest and most extensive way of knowing the other as well as ourself, although without cognitive structure. It is basic to Wittgenstein's ‘aspect-seeing’, that is immediately seeing another person's behaviour as expressive of a human mind. [ABSTRACT FROM AUTHOR]

A commentary on Eugenio Gaddini's paper ‘On imitation’ is given, based on a close reading, involving a detailed description and analysis of its main concepts and themes. [ABSTRACT FROM AUTHOR]

Across the lifespan and across populations, humans 'overimitate' causally unnecessary behaviors. Such irrelevant-action imitation facilitates faithful cultural transmission, but its immediate benefits to the imitator are controversial. Over short time scales, irrelevant-action imitation may bootstrap artifact exploration or interpersonal affiliation, and over longer time scales it may facilitate acquisition of either causal models or social conventions. To investigate these putative functions, we recruited community samples from two under-studied populations: Yasawa, Fiji, and Huatasani, Peru. We use a two-action puzzle box: first after a video demonstration, and again one month later. Treating age as a continuous variable, we reveal divergent developmental trajectories across sites. Yasawans (44 adults, M = 39.9 years, 23 women; 42 children, M = 9.8 years, 26 girls) resemble documented patterns, with irrelevant-action imitation increasing across childhood and plateauing in adulthood. In contrast, Huatasaneños (48 adults, M = 37.6 years, 33 women; 47 children, M = 9.3 years, 13 girls) evince a parabolic trajectory: adults at the site show the lowest irrelevant-action imitation of any demographic set in our sample. In addition, all age sets in both populations reduce their irrelevant actions at Time 2, but do not reduce their relevant-action imitation or goal attainment. Taken together, and considering the local cultural contexts, our results suggest that irrelevant-action imitation serves a short-term function and is sensitive to the social context of the demonstration., (© 2019 John Wiley & Sons Ltd.)

There is a large body of evidence of apparently spontaneous mimicry in humans. This phenomenon has been described as "automatic imitation" and attributed to a mirror neuron system, but there is little direct evidence that it is involuntary rather than intentional. Cook et al. supplied the first such evidence in a unique strategic game design that gave all subjects a pecuniary incentive to avoid imitation [Cook R, Bird G, Lünser G, Huck S, Heyes C (2012) Proc Biol Sci 279(1729):780—786]. Subjects played Rock-Paper-Scissors repeatedly in matches between fixed pairs, sometimes with one and sometimes with both subjects blindfolded. The frequency of draws in the blind-blind condition was at chance, but in the blind-sighted condition it was significantly higher, suggesting automatic imitation had occurred. Automatic imitation would raise novel issues concerning how strategic interactions are modeled in game theory and social science; however, inferring automatic imitation requires significant incentives to avoid it, and subjects' incentives were less than 3 US cents per 60-game match. We replaced Cook et al.'s Rock-Paper-Scissors with a Matching Pennies game, which allows far stronger incentives to avoid imitation for some subjects, with equally strong incentives to imitate for others. Our results are important in providing evidence of automatic imitation against significant incentives. That some of our subjects had incentives to imitate also enables us clearly to distinguish intentional responding from automatic imitation, and we find evidence that both occur. Thus, our results strongly confirm the occurrence of automatic imitation, and illuminate the way that automatic and intentional processes interact in a strategic context. [ABSTRACT FROM AUTHOR]

This paper examines the use of performative verbs meaning “I beg, I ask” and pragmatic markers meaning “please” in the 6th-century Liber vitae patrum by Gregory of Tours. It discusses the frequency of each verb and the possible factors that may have influenced its selection, such as imitation of previous authors, the social status of the communication agents, the requested things, and any difficulty in fulfilling the request. For example, the fact that the highest frequency was found for quaeso (which was perceived as an archaism already in Cicero’s time) suggests that Gregory followed the model of learned men from the 4th and 5th centuries CE who “resuscitated” this verb after its decline in the 2nd and 3rd centuries CE. The paper also examines other additional devices, such as a formula with digneris, and forms of address. [ABSTRACT FROM AUTHOR]

Expert demonstrations in imitation learning often contain different behavioral modes, e.g., driving modes such as driving on the left, keeping the lane, and driving on the right in the driving tasks. Although most existing multi-modal imitation learning methods allow learning from demonstrations of multiple modes, they have strict constraints on the data of each mode, generally requiring a near data ratio of all modes. Otherwise, it tends to fall into a mode collapse or only learn the data distribution of the mode that has the largest data volume. To address the problem, an algorithm that balances real-fake loss and classification loss by modifying the output of the discriminator, referred to as BAlanced Generative Adversarial Imitation Learning (BAGAIL), is proposed. With this modification, the generator is only rewarded for generating real trajectories with correct modes. BAGAIL is therefore able to deal with imbalanced expert demonstrations and carry out efficient learning for each mode. The learning process of BAGAIL is divided into a pre-training stage and an imitation learning stage. During the pre-training stage, BAGAIL initializes the generator parameters by means of conditional Behavioral Cloning, laying the foundation for the direction of parameter optimization. During the imitation learning stage, BAGAIL optimizes the parameters by using the adversary between the generator and the modified discriminator so that the finally obtained policy can successfully learn the distribution of imbalanced expert data. The experiments showed that BAGAIL accurately distinguished different behavioral modes with imbalanced demonstrations. What is more, the learning result of each mode is close to the expert standard and more stable than other multi-modal imitation learning methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The purpose of this paper is to offer a new coding system to capture preschool playful behaviours based on the imitative, exploratory and imaginative aspects underlying playful behaviours (CIEIPO checklist: Imitation, Exploration, Imagination, for Play Observation). A series of focus group sessions (four in total) involving experts in the field were conducted to identify the most typical preschool play behaviours. In addition, a thorough literature review allowed for the operational definition of the items. The number of participants consisted of 280 preschool-aged children, aged 3 to 5 years (Mage = 4.5, SDage = 1.8), 130 of whom were girls. The most frequently observed behaviours were exploratory (150 times) and imaginative (136 times), while imitative behaviours occurred 58 times. The index of agreement between raters was 0.89 (Cohen's Kappa). The results show important implications for promoting play skills in preschool children, with particular relevance to the developmental functions involved (exploratory, symbolic and imitative skills). [ABSTRACT FROM AUTHOR]

Unrealistic optimism bias appears when a person perceives oneself – in comparison to peers – as less at risk from threats. This bias has been widely reported and the consequences are clear: it puts one's health in danger. The existing body of literature proposes egocentrism as a mechanism leading to a reduction in this bias. The present paper tests a novel mechanism orienting a person toward others – thus linked with egocentrism – i.e., mimicry. Results showed directly opposing effects: mimicry induced a stronger tendency to perceive oneself as less threatened. This result is not only surprising but especially alarming since mimicry may be used in patient-doctor dialogue which may backfire, leading to resistance to medical recommendations provided by the doctor. [ABSTRACT FROM AUTHOR]

This paper considers undecidability in the imitation game, the so-called Turing Test. In the Turing Test, a human, a machine, and an interrogator are the players of the game. In our model of the Turing Test, the machine and the interrogator are formalized as Turing machines, allowing us to derive several impossibility results concerning the capabilities of the interrogator. The key issue is that the validity of the Turing test is not attributed to the capability of human or machine, but rather to the capability of the interrogator. In particular, it is shown that no Turing machine can be a perfect interrogator. We also discuss meta-imitation game and imitation game with analog interfaces where both the imitator and the interrogator are mimicked by continuous dynamical systems. [ABSTRACT FROM AUTHOR]

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

This paper attempts to develop a constructive dialogue between moral bioenhancement and Reformed theology of sanctification. According to Reformed theology, human beings are first sanctified by God objectively (passive sanctification) and consequently seek subjectively for their own growth in moral life (active sanctification). Moral bioenhancement and sanctification, thus, share a commonality, that is, emphasizing moral betterment. As such, to the extent that passive sanctification is not obliterated but rather prioritized, moral bioenhancement can be viewed as a consequence of a good work performed freely by the sanctified person to seek for moral growth in the imitation of Christ. [ABSTRACT FROM AUTHOR]

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Adversarial imitation learning (AIL) is a powerful method for automated decision systems due to training a policy efficiently by mimicking expert demonstrations. However, implicit bias is present in the reward function of these algorithms, which leads to sample inefficiency. To solve this issue, an algorithm, referred to as Mutual Information Generative Adversarial Imitation Learning (MI-GAIL), is proposed to correct the biases. In this study, we propose two guidelines for designing an unbiased reward function. Based on these guidelines, we shape the reward function from the discriminator by adding auxiliary information from a potential-based reward function. The primary insight is that the potential-based reward function provides more accurate rewards for actions identified in the two guidelines. We compare our algorithm with SOTA imitation learning algorithms on a family of continuous control tasks. Experiments results show that MI-GAIL is able to address the issue of bias in AIL reward functions and further improve sample efficiency and training stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Volumetric representation is a technique used to express 3D objects in various fields, such as medical applications. On the other hand, tomography images for reconstructing volumetric data have limited utilization because they contain personal information. Existing GAN-based medical image generation techniques can produce virtual tomographic images for volume reconstruction while preserving the patient's privacy. Nevertheless, these images often do not consider vertical correlations between the adjacent slices, leading to erroneous results in 3D reconstruction. Furthermore, while volume generation techniques have been introduced, they often focus on surface modeling, making it challenging to represent the internal anatomical features accurately. This paper proposes volumetric imitation GAN (VI-GAN), which imitates a human anatomical model to generate volumetric data. The primary goal of this model is to capture the attributes and 3D structure, including the external shape, internal slices, and the relationship between the vertical slices of the human anatomical model. The proposed network consists of a generator for feature extraction and up-sampling based on a 3D U-Net and ResNet structure and a 3D-convolution-based LFFB (local feature fusion block). In addition, a discriminator utilizes 3D convolution to evaluate the authenticity of the generated volume compared to the ground truth. VI-GAN also devises reconstruction loss, including feature and similarity losses, to converge the generated volumetric data into a human anatomical model. In this experiment, the CT data of 234 people were used to assess the reliability of the results. When using volume evaluation metrics to measure similarity, VI-GAN generated a volume that realistically represented the human anatomical model compared to existing volume generation methods. [ABSTRACT FROM AUTHOR]

With the rapid development of autonomous driving technology, both self-driven and human-driven vehicles will share roads in the future and complex information exchange among vehicles will be required. Therefore, autonomous vehicles need to behave as similar to human drivers as possible, to ensure that their behavior can be effectively understood by the drivers of other vehicles and be more in line with the cognition of humans on driving behavior. Therefore, this paper studies the evaluation function of human drivers, using the method of inverse reinforcement learning, aiming for the learned behavior to better imitate the behavior of human drivers. At the same time, this paper proposes a semi-Markov model, to extract the intentions of surrounding related vehicles and divides them into defensive and cooperative, leading the vehicle to adopt a reasonable response to different types of driving scenarios. [ABSTRACT FROM AUTHOR]

Digitalization, virtualization, commercialization, loss of integrity, polystylistics, liberation from any norms are the latest trends that determine the development of contemporary art. They influence the functioning of communication models in the postmodern era that evolve in accordance with the achievements of technology and acquire mobility, variability and interactivity. Interaction between social processes and scientific and technological achievements is increasing, the essence of communication in the space of postmodern culture is being rethought, particularly, the boundary between the types of art is being levelled. The latter phenomenon leads to the emergence of new instruments and methods of artistic creativity and expands the possibilities of artistic selfexpression. One of the promising areas of artistic exploration is the creation of sound interactive installations. This paper proposes a classification of communication models that are based on the analysis of interactive sound installations. The classification is based on determining the degree to which audiences actions influence the final sound result of the creative action. The paper claims that the parameters of the exhibition space are important components of the installation's structure and form. A dialogue with nature by way of incorporating sounds of nature, their imitations, and interpretations in art objects is found to be a current trend in interactive sound installations. [ABSTRACT FROM AUTHOR]

Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a series of experiments in collective social robotics, spanning more than 10 years, with the long-term aim of building embodied models of (aspects of) cultural evolution. Initial experiments demonstrated the emergence of behavioural traditions in a group of social robots programmed to imitate each other's behaviours (we call these Copybots). These experiments show that the noisy (i.e. less than perfect fidelity) imitation that comes for free with real physical robots gives rise naturally to variation in social learning. More recent experimental work extends the robots' cognitive capabilities with simulation-based internal models, equipping them with a simple artificial theory of mind. With this extended capability we explore, in our current work, social learning not via imitation but robot–robot storytelling, in an effort to model this very human mode of cultural transmission. In this paper, we give an account of the methods and inspiration for these experiments, the experiments and their results, and an outline of possible directions for this programme of research. It is our hope that this paper stimulates not only discussion but suggestions for hypotheses to test with the Storybots. This article is part of a discussion meeting issue 'The emergence of collective knowledge and cumulative culture in animals, humans and machines'. [ABSTRACT FROM AUTHOR]

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Generative adversarial imitation learning (GAIL) regards imitation learning (IL) as a distribution matching problem between the state-action distributions of the expert policy and the learned policy. In this paper, we focus on the generalization and computational properties of policy classes. We prove that the generalization can be guaranteed in GAIL when the class of policies is well controlled. With the capability of policy generalization, we introduce distributional reinforcement learning (RL) into GAIL and propose the greedy distributional soft gradient (GDSG) algorithm to solve GAIL. The main advantages of GDSG can be summarized as: (1) Q-value overestimation, a crucial factor leading to the instability of GAIL with off-policy training, can be alleviated by distributional RL. (2) By considering the maximum entropy objective, the policy can be improved in terms of performance and sample efficiency through sufficient exploration. Moreover, GDSG attains a sublinear convergence rate to a stationary solution. Comprehensive experimental verification in MuJoCo environments shows that GDSG can mimic expert demonstrations better than previous GAIL variants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This paper reports on the history and use of man-made crystals for jewelry in a contemporary context. The cross-disciplinary contextual review traces human fascination with gemstones back centuries, and covers key developments in their "imitation." The infiltration of these man-made crystals in the market sparked the development of the discipline of gemology, through which they are often painted in a negative light. With innovations in the growth of man-made crystals revolutionizing other industries (engineering, technology, etc.), and their distinction from mined crystals increasingly more challenging, the paper reports on the terminology prescribed to the industry to describe man-made and mined crystals. These descriptions, which include "natural" and "real" versus "synthetic," are interrogated in relation to key texts and a need for the identification of an alternative terminology is proposed. The jewelry industry remains apprehensive about the influx of man-made crystals, and only a small number of man-made crystal suppliers are growing crystals for the purpose of creative exploration beyond the imitation of mined crystals for jewelry. Through the review of artifacts, where man-made crystals have been used, their ability to enable creativity is highlighted, and their further future creative possibilities explored. [ABSTRACT FROM AUTHOR]

In this paper, the design of a five degrees of freedom (5DoF) underwater manipulator system is presented and discussed. The forward and inverse kinematics problem are solved and shown. Additionally, a system that mimics the human arm movements using a manipulator is proposed. In order to imitate the movement of the human arm, the layout diagram of the IMU sensors and the method of first calibration were presented. The work also presents a device with a simple haptic feedback used to facilitate control of the gripper. The presented approach to the design of a robot control system is dedicated to an underwater robot with the main assumption of reducing the complexity of the system. Taking into account the authors' knowledge, this approach distinguishes the presented system from solutions known from the literature. [ABSTRACT FROM AUTHOR]

Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain. This paper presents a study on the design and development of an imitation detection system using an HAR algorithm based on deep learning. This study explores the use of deep learning models, such as a single-frame convolutional neural network (CNN) and pretrained VGG-16, for the accurate classification of human actions. The proposed models were evaluated using a benchmark dataset, KTH. The performance of these models was compared with that of classical classifiers, including K-Nearest Neighbors, Support Vector Machine, and Random Forest. The results showed that the VGG-16 model achieved higher accuracy than the single-frame CNN, with a 98% accuracy rate. [ABSTRACT FROM AUTHOR]

This paper argues that in Kierkegaard's works, in his upbuilding discourses and late journal entries in particular, meekness or gentleness (Danish: Sagtmodighed) is presented as a distinctive moral and spiritual quality that exhibits a number of characteristics that are usually regarded as attributes of a virtue. Following a "grammatical approach" to what counts as a virtue, rather than a specifically Aristotelian-Thomistic interpretation, it is argued that Kierkegaard presents meekness as an encompassing attitude, a character trait, which can be acquired through imitation of exemplary persons, Christ, in particular, which aims for the good life, is conducive of the good, and is for the benefit of others and the self. It is demonstrated that according to Kierkegaard, meekness differs from other virtues such as courage and patience by its forgiving attitude towards the wrongdoer and nonviolent resistance to injustice and evil. As a virtue that disposes a person to nonviolent resistance, meekness has socio-political implications: injustice is uncovered and criticized for the benefit of "the poor". A meek person does not confirm the world in its evil, but criticizes it, albeit in a way that is appropriate to meekness, i.e., in a forgiving and nonviolent way. [ABSTRACT FROM AUTHOR]

Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities. [ABSTRACT FROM AUTHOR]

Social learning often occurs between groups with different levels of experience. Yet little is known about the ideal behavioral rules in such contexts. Existing insights only apply when individuals learn from each other in the same group. In this paper, we close this gap and consider two groups, novices and experienced. Experienced should not learn from novices. For novices learning from experienced, a particular form of probabilistic imitation is selected. Novices should imitate any experienced who is more successful, and sometimes but not always imitate an experienced who is less successful. [ABSTRACT FROM AUTHOR]