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Received：25 August 2020，
Revised：2021-2-3，
Accepted：10 February 2021，
Published：16 December 2021
移动端阅览
机器的情感是通过融入具有情感能力的智能体实现的，虽然目前在人机交互领域已经有大量研究成果，但有关智能体情感计算方面的研究尚处起步阶段，深入开展这项研究对推动人机交互领域的发展具有重要的科学和应用价值。本文通过检索Scopus数据库选择有代表性的文献，重点关注情感在智能体和用户之间的双向流动，分别从智能体对用户的情绪感知和对用户情绪调节的角度开展分析总结。首先梳理了用户情绪的识别方法，即通过用户的表情、语音、姿态、生理信号和文本信息等多通道信息分析用户的情绪状态，归纳了情绪识别中的一些机器学习方法。其次从用户体验角度分析具有情绪表现力的智能体对用户的影响，总结了智能体的情绪生成和表现技术，指出智能体除了通过表情之外，还可以通过注视、姿态、头部运动和手势等非言语动作来表现情绪。并且梳理了典型的智能体情绪架构，举例说明了强化学习在智能体情绪设计中的作用。同时为了验证模型的准确性，比较了已有的情感评估手段和评价指标。最后指出智能体情感计算急需解决的问题。通过对现有研究的总结，智能体情感计算研究是一个很有前景的研究方向，希望本文能够为深入开展相关研究提供借鉴。
Human computer interaction technology has been promoting to realize intelligent human-computer interaction. The user's emotional experience in the human-computer interaction system has been facilitated based on the realization of emotional interaction. Emotional interaction has been intended to use widely via Gartner's analysis next decade. The agent can be real or virtual to detect the user's emotion and adjust the user's emotion. It can greatly enhance the user's experience in human-computer interaction on the aspects of psychological rehabilitation
E-education
digital entertainment
smart home
virtual tourism
E-commerce and etc. The research of agent's affective computing has involved in computer graphics
virtual reality
human-computer-based interaction
machine learning
psychology
social science. Based on Scopus database
2 080 journal papers have been optioned via using virtual human (agent
multimodal) plus emotional interaction as the key words each. The perspective of agent's perceptions and influence of users' emotions have been analyzed and summarized. The importance of multi-channel in emotion perception and the typical machine learning algorithms in emotion recognition have been summarized from the perspective of agent's perception of users' emotions. The external and internal factors affecting users' emotions have been analyzed from the perspective of agent's influence on users' emotions. The emotional architecture
emotional generation and expression algorithms have been implemented. Customized evaluation methods have been applied to improve the accuracy of the affective computing algorithm. The importance of emotional agent in human-computer interaction has been analyzed. Four key steps of agent affective computing have been summarized based on current studies: 1) An agent expressed its emotion to the user. 2) The user gave their feedback to the agent (they may or may not express their satisfaction or dissatisfaction via some channels like facial expressions). 3) The real-time agent perceived the user's emotional state and intention and adjustable emotional performance to respond to user's feedback. 4) A standard (e.g.
the completion of emotion regulation task
the end of plot) has been reached
the agent stopped interacting with the user
otherwise
returns to step 1). The current studies have shown that user's expressed emotions via facial expressions
voices
postures
physiological signals and texts on the aspect of user emotion recognition. The multi-channel method has been more reliable than the single channel method. Machine learning can be used to extract emotional features. Typical machine learning algorithms and their applicable scenarios have been sorted out based on CNN (convolutional neural network) nowadays. Some solutions have been facilitated to resolve insufficient data and over fitting issues. Spatial distance
the number of agents
the appearance of the agent
brightness and shadow have been set as external factors. Agent's autonomous emotion expression has been targeted as the internal factor. An agent should have an emotional architecture and use facial expression
eye gaze
posture
head movement gesture and other channels to express its emotion. The accuracy of the emotional classification model and users' feelings has been assessed based on an affective computing model. The statistical sampling analysis has been listed in the table. The existing emotional agents such as low intelligence
lack of common-sense database
lack of interactivity have been as the constrained factors. The research of agent affective computing in the field of human-computer interaction has been developed further. An affective computing for human-computer-based interaction and an agent could be a channel of emotional interaction. Knowledge-based database and appropriated machine learning algorithms have been adopted to build an agent with the ability of emotion perception and emotion regulation. Qualified physiological detection equipment and sustainable enrichment of emotional information assessment methods have developed the affective computing further.
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