智能人机系统的人机融合交互研究综述

Review of Human-computer Integration Interaction in Intelligent Human-computer System

中文关键词: 人机融合  智能人机系统  认知决策

英文关键词: human-computer integration  intelligent human-computer system  cognitive decision-making

中文摘要:

      目的 对人机融合的研究现状进行了梳理和分析，旨在为智能人机系统研究提供理论支撑。方法 从人机交互和人工智能的概念出发，结合多通道信息融合、人机融合认知决策机制、生理测量和人机融合协作模式等研究，对人机融合和智能人机交互的研究现状、研究热点与趋势进行了分析和归纳。结果 智能机器的信息处理逻辑与人类之间的沟通障碍，抑制了人工智能技术对人机资源整合的工效学意义，人机融合的研究促进了人工智能技术与人机交互的精准合作。结论 智能人机系统中，人、机、物理环境的融合不足是当前学界面临的挑战。人机融合研究应以感知识别、多通道信息融合、认知决策为着力点，帮助机器有效学习、理解、预测人类交互意图，探寻人机对话协作模式的影响要素。人机融合的群体智能对未来人机交互发展有着举足轻重的意义，通过多模态生理融合与自适应的可视化表征机理研究建立认知效率和可视化表征的联系，根据生理指标的变化构建意图空间并指导人机融合协作模式的研究。

英文摘要:

      The research status and progress of human-computer integration is analyzed. This paper analyzes and summarizes the research status, hot spots and trends of human-computer interaction based on the concept of human-computer interaction and artificial intelligence, multi-channel information integration, cognitive decision-making mechanism, physiological measurement and human- computer cooperation mode. The communication barrier between information-processing logic of artificial intelligence and human beings inhibits the ergonomic effect of artificial intelligence technology on human-computer resource integration. The study of human-computer integration promotes the precise cooperation between artificial intelligence technology and human-computer interaction. At the stage of human-on-of-the-loop, the lack of integration of human, computer and physical environment is a challenge for academic community. Human-computer integration research should focus on perception recognition, multi-channel information integration and cognitive decision-making to help computer effectively learn, understand and predict human interaction intentions and explore the influencing factors of human-computer dialogue and cooperation mode. The swarm intelligence of human-computer integration is of great significance to the future development of human-computer interaction. The relationship between cognitive efficiency and visual representation is established by studying the mechanism of multimodal physiological integration and self-adaptive visual representation. The intention space is constructed according to the changes of physiological indicators to prove guidance on the study of human-computer integration cooperation mode.