本文介绍了一种名为“ Hand of Hands”的新颖协作教育游戏的设计，实施和评估，涉及我们设计的儿童和定制的社交机器人（\ Emph {hakshe}）。通过这个游戏平台，我们旨在向儿童讲授适当的手卫生实践，并探索在这种环境中亲社会机器人与儿童之间发生的互动程度。我们将游戏化与计算机作为社会演员（CASA）范式融合在一起，以将机器人作为社会演员或游戏中的其他玩家建模。该游戏是使用Godot的2D引擎和Alice 3开发的。在这项研究中，32名参与者通过视频电视节目平台\ Emph {Zoom}在线玩游戏。为了理解亲社会机器人对儿童互动的影响，我们将研究分为两个条件：裸露和没有裸露。对儿童互动的标题和视频分析的详细分析表明，我们的平台帮助孩子学习了良好的手工卫生实践。我们还发现，尽管学习本身并没有受到机器人的亲社会性影响，但使用亲社会机器人会创造出令人愉悦的互动和更大的社交互动。

人类的感知基于无意识的推论，其中感觉输入与先前的信息集成在一起。这种现象被称为上下文依赖性，有助于面对外部世界的不确定性，并在先前的经验上构建了预测。另一方面，人类的感知过程固有地是由社会互动塑造的。但是，上下文依赖性的机制如何影响到迄今为止未知。如果使用以前的经验 - 先验 - 在单个环境中是有益的，那么它可能代表了其他代理商可能没有相同先验的社会场景中的问题，从而在共享环境上造成了感知的错误。本研究解决了这个问题。我们研究了与人形机器人ICUB的互动环境中的上下文依赖性，该机器人是刺激示威者。参与者在两个条件下重现了机器人所示的长度：一个具有社交性的ICUB，另一个与ICUB充当机械臂。机器人的不同行为显着影响了感知的先验使用。此外，社会机器人通过提高准确性并减少参与者的总体感知错误，从而对感知性能产生积极影响。最后，观察到的现象是按照贝叶斯的方法加深和探索共同感知的新概念进行了建模的。

在这里，我询问我们可以了解如何了解性别如何影响人们如何与机器人互动。我回顾了2018年或更早发布的46个社会机器人的实证研究，报告了其参与者的性别，机器人的感知或预期性别，或两者兼而有之，并对参与者或机器人性别进行了一些分析。从这些研究中，我发现机器人默认被认为是男性，机器人吸收了人类的性别刻板印象，并且男性倾向于比女性更多地与机器人互动。我强调了关于年轻参与者中这种性别影响如何有何不同的开放问题，以及是否应该寻求将机器人的性别与参与者的性别相匹配，以确保积极的互动结果。我的结论是建议未来的研究应：包括性别多样化的参与者池，包括非二进制参与者，依靠自我认同来辨别性别而不是研究人员的感知，控制已知的性别协变量，测试有关性​​别的不同研究结果，并测试使用的机器人是否被参与者视为性别。我包括一个附录，其中包含46篇论文中每一篇与性别相关的发现的叙述摘要，以帮助未来的文学评论。

最近十年表明，人们对机器人作为福祉教练的兴趣越来越大。但是，尚未提出针对机器人设计作为促进心理健康的教练的凝聚力和全面的准则。本文详细介绍了基于基于扎根理论方法的定性荟萃分析的设计和道德建议，该方法是通过三项以用户为中心的涉及机器人福祉教练的三个不同的以用户为中心进行的，即：（1）与参与性设计研究一起进行的。 11名参与者由两位潜在用户组成，他们与人类教练一起参加了简短的专注于解决方案的实践研究，以及不同学科的教练，（2）半结构化的个人访谈数据，这些数据来自20名参加积极心理学干预研究的参与者借助机器人福祉教练胡椒，（3）与3名积极心理学研究的参与者以及2名相关的福祉教练进行了一项参与式设计研究。在进行主题分析和定性荟萃分析之后，我们将收集到收敛性和不同主题的数据整理在一起，并从这些结果中提炼了一套设计准则和道德考虑。我们的发现可以在设计机器人心理福祉教练时考虑到关键方面的关键方面。

在过去的几年中，围绕种族类人体机器人的有问题实践的讨论已经上升。为了彻底理解机器人在人类机器人互动（HRI）社区中如何理解机器人的“性别” - 即如何被操纵，在哪些环境中以及其对人们的看法和人们产生哪些影响的影响，为基础建立基础。与机器人的互动 - 我们对文献进行了范围的评论。我们确定了553篇与我们从5个不同数据库中检索的评论相关的论文。审查论文的最终样本包括2005年至2021年之间的35篇论文，其中涉及3902名参与者。在本文中，我们通过报告有关其性别的目标和假设的信息（即操纵性别的定义和理由），对机器人的“性别”（即性别提示和操纵检查），对性别的定义和理由进行彻底总结这些论文。 （例如，参与者的人口统计学，受雇的机器人）及其结果（即主要和互动效应）。该评论表明，机器人的“性别”不会影响HRI的关键构建，例如可爱和接受，而是对刻板印象产生最强烈的影响。我们利用社会机器人技术和性别研究中的不同认识论背景来提供有关审查结果的全面跨学科观点，并提出了在HRI领域前进的方法。

这项研究提出了新的策略，以研究信任和群体动态在儿童机器人相互作用中的相互影响。我们使用类人机器人ICUB实施了类似游戏的实验活动，并设计了一份问卷来评估孩子如何看待这种相互作用。我们还旨在验证传感器，设置和任务是否适合研究此类方面。问卷的结果表明，年轻人将ICUB视为朋友，通常以积极的方式将ICUB视为朋友。其他初步结果表明，通常，孩子在活动期间信任ICUB，并且在其错误后，他们试图用诸如：“不用担心ICUB，我们原谅您”之类的句子来放心。此外，对机器人在小组认知活动中的信任似乎会根据性别而发生变化：在机器人连续两个错误之后，女孩倾向于比男孩更信任ICUB。最后，跨游戏计算的点和自我报告的量表之间的不同年龄组之间没有明显的差异。我们提出的工具适合研究不同年龄段的人类机器人相互作用（HRI）的信任，并且似乎适合理解小组相互作用的信任机制。

Taking advice from others requires confidence in their competence. This is important for interaction with peers, but also for collaboration with social robots and artificial agents. Nonetheless, we do not always have access to information about others' competence or performance. In these uncertain environments, do our prior beliefs about the nature and the competence of our interacting partners modulate our willingness to rely on their judgments? In a joint perceptual decision making task, participants made perceptual judgments and observed the simulated estimates of either a human participant, a social humanoid robot or a computer. Then they could modify their estimates based on this feedback. Results show participants' belief about the nature of their partner biased their compliance with its judgments: participants were more influenced by the social robot than human and computer partners. This difference emerged strongly at the very beginning of the task and decreased with repeated exposure to empirical feedback on the partner's responses, disclosing the role of prior beliefs in social influence under uncertainty. Furthermore, the results of our functional task suggest an important difference between human-human and human-robot interaction in the absence of overt socially relevant signal from the partner: the former is modulated by social normative mechanisms, whereas the latter is guided by purely informational mechanisms linked to the perceived competence of the partner.

本文详细概述了将连续学习（CL）应用于单课的人类机器人互动（HRI）会议（AVG。31 +-10分钟）的案例研究，其中机器人的心理健康教练是积极的（n = 20）参与者的心理学（PP）练习。我们介绍了互动会议后与参与者进行的简短半结构访谈记录的数据的主题分析（TA）的结果，以及对统计结果的分析，证明了参与者的个性如何影响他们如何看待机器人的方式及其互动。

Gender/ing guides how we view ourselves, the world around us, and each other--including non-humans. Critical voices have raised the alarm about stereotyped gendering in the design of socially embodied artificial agents like voice assistants, conversational agents, and robots. Yet, little is known about how this plays out in research and to what extent. As a first step, we critically reviewed the case of Pepper, a gender-ambiguous humanoid robot. We conducted a systematic review (n=75) involving meta-synthesis and content analysis, examining how participants and researchers gendered Pepper through stated and unstated signifiers and pronoun usage. We found that ascriptions of Pepper's gender were inconsistent, limited, and at times discordant, with little evidence of conscious gendering and some indication of researcher influence on participant gendering. We offer six challenges driving the state of affairs and a practical framework coupled with a critical checklist for centering gender in research on artificial agents.

Intelligent agents have great potential as facilitators of group conversation among older adults. However, little is known about how to design agents for this purpose and user group, especially in terms of agent embodiment. To this end, we conducted a mixed methods study of older adults' reactions to voice and body in a group conversation facilitation agent. Two agent forms with the same underlying artificial intelligence (AI) and voice system were compared: a humanoid robot and a voice assistant. One preliminary study (total n=24) and one experimental study comparing voice and body morphologies (n=36) were conducted with older adults and an experienced human facilitator. Findings revealed that the artificiality of the agent, regardless of its form, was beneficial for the socially uncomfortable task of conversation facilitation. Even so, talkative personality types had a poorer experience with the "bodied" robot version. Design implications and supplementary reactions, especially to agent voice, are also discussed.

事实证明，在学习环境中，社会智能代理（SIA）的部署在不同的应用领域具有多个优势。社会代理创作工具使场景设计师能够创造出对SIAS行为的高度控制的量身定制体验，但是，另一方面，这是有代价的，因为该方案及其创作的复杂性可能变得霸道。在本文中，我们介绍了可解释的社会代理创作工具的概念，目的是分析社会代理的创作工具是否可以理解和解释。为此，我们检查了创作工具Fatima-Toolkit是否可以理解，并且从作者的角度来看，其创作步骤可以解释。我们进行了两项用户研究，以定量评估Fatima-Toolkit的解释性，可理解性和透明度，从场景设计师的角度来看。关键发现之一是，法蒂玛 - 库尔基特（Fatima-Toolkit）的概念模型通常是可以理解的，但是基于情感的概念并不那么容易理解和使用。尽管关于Fatima-Toolkit的解释性有一些积极的方面，但仍需要取得进展，以实现完全可以解释的社会代理商创作工具。我们提供一组关键概念和可能的解决方案，可以指导开发人员构建此类工具。

情绪可以提供自然的交流方式，以补充许多领域中社交机器人（例如文本和语音）现有的多模式能力。我们与112、223和151名参与者进行了三项在线研究，以调查使用情绪作为搜救（SAR）机器人的交流方式的好处。在第一个实验中，我们研究了通过机器人的情绪传达与SAR情况有关的信息的可行性，从而导致了从SAR情况到情绪的映射。第二项研究使用控制控制理论是推导此类映射的替代方法。此方法更灵活，例如允许对不同的情绪集和不同机器人进行调整。在第三个实验中，我们使用LED作为表达通道为外观受限的室外现场研究机器人创建了情感表达。在各种模拟的SAR情况下，使用这些情感表达式，我们评估了这些表达式对参与者（采用救援人员的作用）的影响。我们的结果和提议的方法提供了（a）有关情感如何帮助在SAR背景下传达信息的见解，以及（b）在（模拟）SAR通信环境中添加情绪为传播方式的有效性的证据。

在人类居住的环境中使用机器人的挑战是设计对人类互动引起的扰动且鲁棒的设计行为。我们的想法是用内在动机（IM）拟订机器人，以便它可以处理新的情况，并作为人类的真正社交，因此对人类互动伙伴感兴趣。人机互动（HRI）实验主要关注脚本或远程机器人，这是模拟特性，如IM来控制孤立的行为因素。本文介绍了一个“机器人学家”的研究设计，允许比较自主生成的行为彼此，而且首次评估机器人中基于IM的生成行为的人类感知。我们在受试者内部用户学习（n = 24），参与者与具有不同行为制度的完全自主的Sphero BB8机器人互动：一个实现自适应，本质上动机的行为，另一个是反应性的，但不是自适应。机器人及其行为是故意最小的，以专注于IM诱导的效果。与反应基线行为相比，相互作用后问卷的定量分析表明对尺寸“温暖”的显着提高。温暖被认为是人类社会认知中社会态度形成的主要维度。一种被认为是温暖（友好，值得信赖的）的人体验更积极的社交互动。

Students' ability to ask curious questions is a crucial skill that improves their learning processes. To train this skill, previous research has used a conversational agent that propose specific cues to prompt children's curiosity during learning. Despite showing pedagogical efficiency, this method is still limited since it relies on generating the said prompts by hand for each educational resource, which can be a very long and costly process. In this context, we leverage the advances in the natural language processing field and explore using a large language model (GPT-3) to automate the generation of this agent's curiosity-prompting cues to help children ask more and deeper questions. We then used this study to investigate a different curiosity-prompting behavior for the agent. The study was conducted with 75 students aged between 9 and 10. They either interacted with a hand-crafted conversational agent that proposes "closed" manually-extracted cues leading to predefined questions, a GPT-3-driven one that proposes the same type of cues, or a GPT-3-driven one that proposes "open" cues that can lead to several possible questions. Results showed a similar question-asking performance between children who had the two "closed" agents, but a significantly better one for participants with the "open" agent. Our first results suggest the validity of using GPT-3 to facilitate the implementation of curiosity-stimulating learning technologies. In a second step, we also show that GPT-3 can be efficient in proposing the relevant open cues that leave children with more autonomy to express their curiosity.

最后，这项工作将包括对解释的上下文形式的调查。在这项研究中，我们将包括一个时间障碍的方案，其中将测试不同水平的理解水平，以使我们能够评估合适且可理解的解释。为此，我们提出了不同的理解水平（lou）。用户研究将旨在比较不同的LOU在不同的互动环境中。将研究同时医院环境的用户研究。

Using robots in educational contexts has already shown to be beneficial for a student's learning and social behaviour. For levitating them to the next level of providing more effective and human-like tutoring, the ability to adapt to the user and to express proactivity is fundamental. By acting proactively, intelligent robotic tutors anticipate possible situations where problems for the student may arise and act in advance for preventing negative outcomes. Still, the decisions of when and how to behave proactively are open questions. Therefore, this paper deals with the investigation of how the student's cognitive-affective states can be used by a robotic tutor for triggering proactive tutoring dialogue. In doing so, it is aimed to improve the learning experience. For this reason, a concept learning task scenario was observed where a robotic assistant proactively helped when negative user states were detected. In a learning task, the user's states of frustration and confusion were deemed to have negative effects on the outcome of the task and were used to trigger proactive behaviour. In an empirical user study with 40 undergraduate and doctoral students, we studied whether the initiation of proactive behaviour after the detection of signs of confusion and frustration improves the student's concentration and trust in the agent. Additionally, we investigated which level of proactive dialogue is useful for promoting the student's concentration and trust. The results show that high proactive behaviour harms trust, especially when triggered during negative cognitive-affective states but contributes to keeping the student focused on the task when triggered in these states. Based on our study results, we further discuss future steps for improving the proactive assistance of robotic tutoring systems.

人类不断受到他人的行为和观点的影响。至关重要的是，人类之间的社会影响是由互惠构成的：我们更多地遵循一直在考虑我们意见的人的建议。在当前的工作中，我们研究了与社会类人机器人互动时相互影响的影响是否可以出现。在一项联合任务中，人类参与者和人形机器人进行了感知估计，然后在观察伴侣的判断后可以公开修改它们。结果表明，赋予机器人表达和调节其对人类判断的易感水平的能力代表了双刃剑。一方面，当机器人遵循他们的建议时，参与者对机器人的能力失去了信心。另一方面，参与者不愿透露他们对易感机器人缺乏信心，这表明出现了支持人类机器人合作的社会影响力的相互机制。

Training facial emotion recognition models requires large sets of data and costly annotation processes. To alleviate this problem, we developed a gamified method of acquiring annotated facial emotion data without an explicit labeling effort by humans. The game, which we named Facegame, challenges the players to imitate a displayed image of a face that portrays a particular basic emotion. Every round played by the player creates new data that consists of a set of facial features and landmarks, already annotated with the emotion label of the target facial expression. Such an approach effectively creates a robust, sustainable, and continuous machine learning training process. We evaluated Facegame with an experiment that revealed several contributions to the field of affective computing. First, the gamified data collection approach allowed us to access a rich variation of facial expressions of each basic emotion due to the natural variations in the players' facial expressions and their expressive abilities. We report improved accuracy when the collected data were used to enrich well-known in-the-wild facial emotion datasets and consecutively used for training facial emotion recognition models. Second, the natural language prescription method used by the Facegame constitutes a novel approach for interpretable explainability that can be applied to any facial emotion recognition model. Finally, we observed significant improvements in the facial emotion perception and expression skills of the players through repeated game play.

MetaVerse，巨大的虚拟物理网络空间，为艺术家带来了前所未有的机会，将我们的身体环境的每个角落与数字创造力混合。本文对计算艺术进行了全面的调查，其中七个关键主题与成权相关，描述了混合虚拟物理现实中的新颖艺术品。主题首先涵盖了MetaVerse的建筑元素，例如虚拟场景和字符，听觉，文本元素。接下来，已经反映了诸如沉浸式艺术，机器人艺术和其他用户以其他用户的方法提供了沉浸式艺术，机器人艺术和其他用户中心的若干非凡类型的新颖创作。最后，我们提出了几项研究议程：民主化的计算艺术，数字隐私和搬迁艺术家的安全性，为数字艺术品，技术挑战等等的所有权认可。该调查还担任艺术家和搬迁技术人员的介绍材料，以开始在超现实主义网络空间领域创造。

Using chatbots to deliver recommendations is increasingly popular. The design of recommendation chatbots has primarily been taking an information-centric approach by focusing on the recommended content per se. Limited attention is on how social connection and relational strategies, such as self-disclosure from a chatbot, may influence users' perception and acceptance of the recommendation. In this work, we designed, implemented, and evaluated a social chatbot capable of performing three different levels of self-disclosure: factual information (low), cognitive opinions (medium), and emotions (high). In the evaluation, we recruited 372 participants to converse with the chatbot on two topics: movies and COVID-19 experiences. In each topic, the chatbot performed small talks and made recommendations relevant to the topic. Participants were randomly assigned to four experimental conditions where the chatbot used factual, cognitive, emotional, and adaptive strategies to perform self-disclosures. By training a text classifier to identify users' level of self-disclosure in real-time, the adaptive chatbot can dynamically match its self-disclosure to the level of disclosure exhibited by the users. Our results show that users reciprocate with higher-level self-disclosure when a recommendation chatbot consistently displays emotions throughout the conversation. Chatbot's emotional disclosure also led to increased interactional enjoyment and more positive interpersonal perception towards the bot, fostering a stronger human-chatbot relationship and thus leading to increased recommendation effectiveness, including a higher tendency to accept the recommendation. We discuss the understandings obtained and implications to future design.