情绪对人类智能非常重要。例如,情绪与内部身体状态和外部刺激的评估密切相关。这有助于我们快速响应环境。人类智能中另一个重要的角色是情绪在决策中的作用。此外,情绪的社会方面也非常重要。因此,如果阐明情绪的机制,我们就可以朝着对自然智慧的本质理解前进。在这项研究中,提出了一种模式的情绪,通过计算模型阐明情绪的机制。此外,从伙伴机器人的角度来看,情感模型可以帮助我们建立能够对人类产生共鸣的机器人。为了理解和同情人们的感受,机器人需要拥有自己的情感。这可能允许机器人在人类社会中被接受。所提出的模型使用由三个模块组成的深度神经网络实现,这三个模块彼此相互作用。仿真结果表明,所提出的模型表现出合理的行为作为情感的基本机制。
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Computer models can be used to investigate the role of emotion in learning. Here we present EARL, our framework for the systematic study of the relation between emotion, adaptation and reinforcement learning (RL). EARL enables the study of, among other things, communicated affect as reinforcement to the robot; the focus of this chapter. In humans, emotions are crucial to learning. For example, a parent-observing a child-uses emotional expression to encourage or discourage specific behaviors. Emotional expression can therefore be a reinforcement signal to a child. We hypothesize that affective facial expressions facilitate robot learning, and compare a social setting with a non-social one to test this. The non-social setting consists of a simulated robot that learns to solve a typical RL task in a continuous grid-world environment. The social setting additionally consists of a human (parent) observing the simulated robot (child). The human's emotional expressions are analyzed in real time and converted to an additional reinforcement signal used by the robot; positive expressions result in reward, negative expressions in punishment. We quantitatively show that the "social robot" indeed learns to solve its task significantly faster than its "non-social sibling". We conclude that this presents strong evidence for the potential benefit of affective communication with humans in the reinforcement learning loop.
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随着人工智能(AI)的发展,人工智能应用极大地影响和改变了人们的日常生活。在这里,首次提出了一种整合了情感机器人,社交机器人,可穿戴式和可穿戴式2.0的可穿戴式机器人。拟议的可穿戴情感机器人适用于广大人群,我们相信它可以在精神层面上改善人类健康,同时满足时尚要求。本文从硬件和算法的角度出发,介绍了一种被称为Fitbot的创新型可穿戴情感机器人的体系结构和设计。此外,从硬件设计,脑电数据采集与分析,用户行为感知,算法部署等方面介绍了机器人脑可穿戴设备的重要功能组件。然后,实现了基于脑电图的用户行为认知。通过不断获取深度,广度的数据,我们提出的Fitbot可以逐步丰富用户生活建模,使可穿戴机器人能够识别用户的意图并进一步了解用户情感背后的行为动机。嵌入Fitbot的生命建模学习算法可以实现更好的用户对情感社交互动的体验。最后,讨论了可穿戴情感机器人的应用服务场景和一些具有挑战性的问题。
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Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Affective computing concerns multidisciplinary knowledge background such as psychology, cognitive, physiology and computer sciences. The paper is emphasized on the several issues involved implicitly in the whole interactive feedback loop. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are also discussed.
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This work proposes an initial memory model for a long-term artificial companion, which migrates among virtual and robot platforms based on the context of interactions with the human user. This memory model enables the companion to remember events that are relevant or significant to itself or to the user. For other events which are either ethically sensitive or with a lower long-term value, the memory model supports forgetting through the processes of generalisation and memory restructuring. The proposed memory model draws inspiration from the human short-term and long-term memories. The short-term memory will support companions in focusing on the stimuli that are relevant to their current active goals within the environment. The long-term memory will contain episodic events that are chronologically sequenced and derived from the companion's interaction history both with the environment and the user. There are two key questions that we try to address in this work: 1) What information should the companion remember in order to generate appropriate behaviours and thus smooth the interaction with the user? And, 2) What are the relevant aspects to take into consideration during the design of memory for a companion that can have different types of virtual and physical bodies? Finally, we show an implementation plan of the memory model, focusing on issues of information grounding, activation and sensing based on specific hardware platforms.
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机器人辅助治疗(RAT)已经成功地用于HRI研究,其中包括社交机器人在医疗保健干预中,因为它们能够吸引人类用户的社交和情感维度。该主题的研究项目遍布全球,遍及美国,欧洲和亚洲。所有这些项目都有一个雄心勃勃的目标,即增加无辜人口的福祉。 RAT的典型工作是使用遥控机器人进行的;一种名为Wizard of of Oz(WoZ)的技术。机器人通常由操作人员控制,患者不知道。然而,从长远来看,WoZ已被证明不是一种可持续发展的技术。为机器人提供自主权(同时保持在治疗师的监督下)有可能减轻治疗师的负担,不仅在治疗方面本身,而且在长期诊断任务中。因此,需要探索在治疗中使用的社交机器人的几个自治程度。增加机器人的自主权也可能带来一系列新的挑战。特别是,需要回答新的道德问题,即使用弱势群体的机器人,以及需要确保符合道德规范的机器人行为。因此,在本次研讨会中,我们希望收集研究结果并探索哪种程度的自主权可能有助于改善医疗保健干预措施,以及我们如何克服其固有的道德挑战。
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情感概念在我们的日常生活中发挥着重要作用,因为他们参与了许多认知过程:从不同学习过程和自然交流的环境感知。社交机器人需要与人交流,这也增加了在许多日常任务中采用不同情感概念的情感模仿模型的普及。然而,这些解决方案的发展与复杂情绪评估系统的整合和发展之间仍然存在差距,真正的社交机器人是必要的。在本文中,我们提出了一个深层神经模型,它是根据情感概念的发展学习的不同方面设计的,为内部和外部情绪评估提供综合解决方案。我们用不同的挑战语料库评估所提出的模型的性能,并将其与用于外部情绪评估的最新模型进行比较。为了扩展对拟议模型的评估,我们设计并收集了一个基于人机交互(HRI)场景的新数据集。我们在iCub机器人中部署了该模型,并评估了机器人基于观察学习和描述不同人的情感行为的能力。所进行的实验表明,所提出的模型与一般情感性行为的最新状态具有竞争性。此外,它能够产生随时间演变的内在情感概念:它不断形成和更新形成的情感概念,这是创建基于机器人体验的情感评估模型的一步。
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推文,博客文章或产品评论的情感极性变得极具吸引力,并在推荐系统,市场预测,商业智能等方面得到应用。深度学习技术正在成为分析此类文本的最佳表现者。然而,在文本挖掘和文本极化分析中有效地使用深度神经网络需要解决几个问题。首先,需要为深度神经网络提供大小和正确标记的数据集。其次,关于字嵌入向量的使用存在各种不确定性:它们是否应该从用于训练模型的相同数据集生成,还是更适合从大型和流行的集合中获取它们?第三,为了简化模型创建,使通用神经网络架构有效并且可以适应各种文本,封装大部分设计复杂性是很方便的。本文针对上述问题,提出了利用神经网络进行情感分析和实现最新技术成果的方法论实践见解。关于第一个问题,探讨了各种众包替代方案的有效性,并利用社交标准创建了双胞胎大小和情感标记的歌曲数据集。为了解决第二个问题,进行了一系列具有各种内容和域的大文本集的实验,尝试各种参数的插入。关于第三个问题,进行了一系列涉及卷积和最大汇集神经层的实验。将单词,双字母和三元组的卷积与几个堆栈中的区域最大汇集层相结合产生了最好的结果。派生体系结构在电影,商业和产品评论的情感极性分析中实现了竞争性表现。
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情绪与动机和行为的适应性密切相关,许多动物物种在行为中表现出情绪的证据。因此,情绪必须与有助于生存的强大机制相关联,并且情绪必须是进化的连续现象。情感如何以及为什么会在自然中融入,事件如何得到情感评估,情感如何与认知复杂性相关,以及它们如何影响行为和学习?在本文中,我提出所有情绪都是奖励处理的表现形式,特别是时间差异( TD)错误评估。执行学习(RL)是一种强大的计算模型,用于通过探索和反馈来学习目标导向的任务。证据表明许多动物物种中存在类似RL的过程。关于RL反馈处理的关键是TD错误的概念,与先前预期(或者估计的效用或损失的估计收益或损失)相比,评估了情况刚好变得多好或多少。 newevidence)。我提出了一种TDRL情绪理论,并讨论了它对人类,动物和机器中情绪的理解,以及其支持中的心理学,神经生物学和计算证据。
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大数据的出现使我们能够通过应用情感计算从统计角度评估各种人类情感。在这项研究中,提出了一种从不同地方的大规模地理参考照片中提取人类情感的新框架。在基于用户生成的足迹收集的社交媒体网站的空间聚类构建之后,利用在线认知服务利用最先进的计算机视觉技术从面部表情中提取人文动机。并且定义了两个幸福指标用于测量不同地方的人文情绪。为了验证该框架的可行性,以世界各地的80个旅游景点为例,以及根据600多万张照片中检测到的超过200万张面孔的人类情感,生成幸福的地方列表。通过考虑不同类型的地理环境,找出人类情感与环境因素之间的关系。结果表明,不同地方的大部分情感变异可以用一些因素来解释,比如开放性。该研究可以提供关于整合人类情感的见解,以丰富对地理和地方GIS中的地方感的理解。
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T wo channels have been distinguished in human interaction [1]: one transmits explicit messages , which may be about anything or nothing ; the other transmits implicit messages about the speakers themselves. Both linguistics and technology have invested enormous efforts in understanding the first, explicit channel, but the second is not as well understood. Understanding the other party's emotions is one of the key tasks associated with the second, implicit channel. To tackle that task, signal processing and analysis techniques have to be developed, while, at the same time, consolidating psychological and linguistic analyses of emotion. This article examines basic issues in those areas. It is motivated by the PHYSTA project, in which we aim to develop a hybrid system capable of using information from faces and voices to recognize people's emotions. The human sciences contain a bank of literature on emotion which is large, but fragmented. The main sources which are relevant to our approach are in psychology and linguistics, with some input from biology. Translating abstract proposals into a working model system is a rational way of consolidating that knowledge base. That approach has several attractions, particularly when referring to hybrid systems, which include symbolic and subsymbolic techniques. First, building an emotion detection system makes it possible to assess the extent to which theoretical proposals explain people's everyday competence at understanding emotion. So long as it is technically impossible to apply that kind of test, theories can only be assessed against their success or failure on selected examples, and that is not necessarily a constructive approach. Second, model building enforces coherence. At a straightforward level, it provides a motivation to integrate information from sources that tend to be kept separate. It can also have subtler effects, such as showing that apparently meaningful ideas are actually difficult to integrate , that conjunctions which seem difficult are quite possible, or that verbal distinctions and debates actually reduce to very little. Hybrid systems have a particular attraction in that they offer the prospect of linking two types of elements that are prominent in reactions to emotion-articulate verbal descriptions and explanations and responses that are felt rather than articulated, which it is natural to think of as subsymbolic. Another related major issue is the emergence of meaning from subsymbolic operations. Intuitively, meanings related to emotion seem to straddle the boundary between the logical, discrete, linguistic representations that classical computing handles neatly (perhaps too neatly to model human cognition well), and the fuzzy, subsymbolic representations that, for example, artificial neural networks construct. That makes the domain of emotion a useful testbed for technologies which aim to create a seamless hybrid environment , in which it is possible for something that deserves th
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Big data: A survey
分类:
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This is the fourth workshop on Knowledge and Reasoning in Practical Dialogue Systems. The first workshop was organised at IJCAI-99 in Stockholm, 1 the second workshop took place at IJCAI-2001 in Seattle, 2 and the third workshop was held at IJCAI-2003 in Acapulco. 3 The current workshop includes research in three main areas: dialogue management , adaptive discourse planning, and automatic learning of dialogue policies. Probabilistic and machine learning techniques have significant representation , and the main applications are in robotics and information-providing systems. These workshop notes contain 12 papers that address these issues from various viewpoints. The papers provide stimulating ideas and we believe that they function as a fruitful basis for discussions and further research. The program committee consisted of the colleagues listed below, who were assisted by three additional reviewers. Without the time spent reviewing the submissions and the thoughtful comments provided by these colleagues, the decision process would have been much more difficult. We would like to express our warmest thanks to them all. 1 Selected contributions have been published in a special issue of ETAI, the Electronic Transaction of Artificial Intelligence
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Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge-either hand-coded knowledge provided by experts or "mined" knowledge learned from the behavior of consumers-to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the additional knowledge required from the database, the ways the recommendations are presented to consumers, the technologies used to create the recommendations, and the level of personalization of the recommendations. We identify five commonly used E-commerce recommender application models, describe several open research problems in the field of recom-mender systems, and examine privacy implications of recommender systems technology.
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In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely Deep Learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.
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研究和实际应用的洪水将社交媒体数据用于广泛的公共应用,包括环境监测,水资源管理,灾难和应急响应。水文信息技术可以利用社交媒体技术,利用新出现的数据,技术和分析工具来处理大型数据集,本文首先提出了一个4W(What,Why,When,hoW)模型和方法结构,以更好地理解和表示社交媒体在水文信息学中的应用,然后提供应用社会的学术研究的概述。媒体到水文信息学,如水环境,水资源,洪水,干旱和水资源稀缺管理。最后,基于前面的讨论,水文信息管理人员和研究人员提出了数据收集,数据质量管理,虚假新闻检测,隐私问题,算法和平台等与水有关的社交媒体应用的一些高级主题和建议。
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The widespread proliferation of handheld devices enables mobile carriers to be connected at anytime and anywhere. Meanwhile, the mobility patterns of mobile devices strongly depend on the users' movements, which are closely related to their social relationships and behaviors. Consequently, today's mobile networks are becoming increasingly human centric. This leads to the emergence of a new field which we call socially-aware networking (SAN). One of the major features of SAN is that social awareness becomes indispensable information for the design of networking solutions. This emerging paradigm is applicable to various types of networks (e.g. opportunistic networks, mobile social networks, delay tolerant networks, ad hoc networks, etc) where the users have social relationships and interactions. By exploiting social properties of nodes, SAN can provide better networking support to innovative applications and services. In addition, it facilitates the convergence of human society and cyber physical systems. In this paper, for the first time, to the best of our knowledge, we present a survey of this emerging field. Basic concepts of SAN are introduced. We intend to generalize the widely-used social properties in this regard. The state-of-the-art research on SAN is reviewed with focus on three aspects: routing and forwarding, incentive mechanisms and data dissemination. Some important open issues with respect to mobile social sensing and learning, privacy, node selfishness and scalability are discussed.
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One of the potent personalization technologies powering the adap-tive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.
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本文通过讨论移动大数据(MBD)挑战的见解,探讨了基于机器学习的移动大数据分析的需求和发展。此外,它还回顾了MBD领域数据分析的最新应用。首先,我们介绍了MBD的发展。其次,回顾了经常采用的数据分析方法。分别介绍了MBD分析的三种典型应用,即无线信道建模,人体在线和离线行为分析,以及车辆互联网中的语音识别。最后,总结了移动大数据分析的主要挑战和未来发展方向。
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