With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances.These estimations are crucial to evaluate each agent's capabilities and, thus, required to facilitate effective collaboration. So far, research in the field of human-AI interaction estimates the perceived difficulty of humans and AI independently from each other. However, the effective interaction of human and AI agents depends on metrics that accurately reflect each agent's perceived difficulty in achieving valuable outcomes. Research to date has not yet adequately examined the differences in the perceived difficulty of humans and AI. Thus, this work reviews recent research on the perceived difficulty in human-AI interaction and contributing factors to consistently compare each agent's perceived difficulty, e.g., creating the same prerequisites. Furthermore, we present an experimental design to thoroughly examine the perceived difficulty of both agents and contribute to a better understanding of the design of such systems.
翻译:随着人工智能(AI)在产业和社会中越来越广泛的采用,有效的人机交互系统变得越来越重要。人类与人工智能相互作用的一个核心挑战是估计单个任务实例的难度,以确定每个代理的能力,从而促进有效的协作。到目前为止,人机交互领域的研究独立估计人类和人工智能的知觉难度。然而,人工智能和人类代理的有效交互取决于准确反映每个代理在实现有价值的结果时的知觉难度的指标。迄今为止的研究还没有充分探究人工智能和人类在知觉难度方面的差异。因此,本研究回顾了关于人工智能与人类交互中知觉难度和影响因素的最新研究,以一致地比较每个代理的知觉难度,例如创建相同的先决条件。此外,我们提出了一种实验设计,彻底研究两个代理的知觉难度,并为更好地理解这种系统的设计做出贡献。