摘要
人工智能技术的快速发展,催生从人机交互(Human-Machine Interaction,HMI)到人机协同(Human-Machine Collaboration,HMC)的根本性转变.在人机协同的框架下,核心目标即充分发挥人类智慧和机器智能的互补优势,以实现任务执行的最大化效率.从哲学及神经科学视角探讨人类与机器在智能表现上的根本差异,定义人机交互中人类的两种典型行为模式:基于技能的直觉行为与基于知识的智性行为.结合人类的直觉优势与机器类人智能优势,定义交互流的概念,并据此构建复杂人机任务分配认知环路模型.通过神经生理学试验,测量并比较专家与新手在特定任务执行时的脑电图(Electroencephalograph,EEG)活动,总结人机交互过程中人类行为模式及对应神经模式差异,初步验证所构建模型合理性.
Abstract
The rapid development of artificial intelligence technology has led to a fundamental transformation from Human-Machine Interaction(HMI)to Human-Machine Collaboration(HMC).The HMC's core objective is to fully leverage the comple-mentary advantages of human wisdom and machine intelligence to maximize the efficiency of task execution within the framework of human-machine collaboration.The essential differences in intelligent performance between humans and machines were explored from the perspectives of philosophy and neuroscience.Two typical modes of human behavior in human-machine interaction were clearly defined:skill-based intuitive behavior and knowledge-based intellectual behavior.The concept of"interaction flow"was brought forward combined human intuition with machine humanoid intelligence.A cognitive loop model was constructed for com-plex human-machine task allocation.The EEG(Electroencephalograph)activities of experts and novices during specific task exe-cution were measured by neurophysiological experiments.The differences in their behavioral patterns and corresponding neural patterns were compared and summarized,which preliminarily validated the rationality of the constructed model.
基金项目
国家重点研发计划(2022YFB3303301)
国家社会科学基金后期资助项目(21FYSB037)
NETL
NSTL
万方数据