论文标题
情报时代人为因素科学的新研究范例和议程
New research paradigms and agenda of human factors science in the intelligence era
论文作者
论文摘要
本文提出了“人为因素科学”的创新概念，以表征工程心理学，人为因素工程，人为计算机互动和其他类似领域的概念。尽管这些领域的观点有所不同，但它们具有共同的方法：“以人为本的设计”。在人工智能时代，人机关系为“人类团队组成”提出了跨时代的进化。这种变化提出了对人为因素科学的挑战，迫使我们重新检查当前的研究范例和议程。根据我们以前的工作，本文提出了三个研究范式：（1）人类联合认知系统：这将智能代理作为具有一定程度的认知能力的认知剂。人类系统系统可以被描述为一种共同的认知系统，在该系统中，人类和智能代理商是合作的队友。 （2）人类AI联合认知生态系统：具有多个人类系统的智能生态系统可以表示为人类AI联合认知生态系统。生态系统的整体性能取决于多个人类系统的最佳协作和设计。 （3）智能社会技术系统（ISTS）：人类系统是在ISTS环境中设计，开发和部署的。 ISTS环境中人类系统的成功设计，开发和部署取决于子系统之间的协同优化。本文期待从三个方面：人类互动，智能人机界面和人类AI团队的人类因素科学的未来研究议程。分析表明，三个新的研究范例将使未来的人类因素科学研究受益。我们认为，拟议的研究范例和未来的研究议程将相互促进，进一步推动了人工智能时代的人为因素科学。
This paper proposes the innovative concept of "human factors science" to characterize engineering psychology, human factors engineering, human-computer interaction, and other similar fields. Although the perspectives in these fields differ, they share a common approach: "human-centered design." In the AI era, the human-machine relationship presents a trans-era evolution to "human-AI teaming." The change has raised challenges for human factors science, compelling us to re-examine current research paradigms and agendas. Based on our previous work, this paper proposes three research paradigms: (1) human-AI joint cognitive systems: this regards an intelligent agent as a cognitive agent with a certain level of cognitive capabilities. A human-AI system can be characterized as a joint cognitive system in which humans and intelligent agents work as teammates for collaboration; (2) human-AI joint cognitive ecosystems: an intelligent ecosystem with multiple human-AI systems can be represented as a human-AI joint cognitive ecosystem. The overall performance of the ecosystem depends on optima collaboration and design across the multiple human-AI systems; (3) intelligent sociotechnical systems (iSTS): human-AI systems are design, developed, and deployed in an iSTS environment. The successful design, development, and deployment of a human-AI system within an iSTS environment depends on the synergistic optimization between the subsystems. This paper looks forward to the future research agenda of human factors science from three aspects: human-AI interaction, intelligent human-machine interface, and human-AI teaming. Analyses show that the three new research paradigms will benefit future research in human factors science. We believe the proposed research paradigms and the future research agenda will mutually promote each other, further advancing human factors science in the AI era.
