Building machines capable of efficiently collaborating with humans has been a longstanding goal in artificial intelligence. Especially in the presence of uncertainties, optimal cooperation often requires that humans and artificial agents model each other's behavior and use these models to infer underlying goals, beliefs or intentions, potentially involving multiple levels of recursion. Empirical evidence for such higher-order cognition in human behavior is also provided by previous works in cognitive science, linguistics, and robotics. We advocate for a new paradigm for active learning for human feedback that utilises humans as active data sources while accounting for their higher levels of agency. In particular, we discuss how increasing level of agency results in qualitatively different forms of rational communication between an active learning system and a teacher. Additionally, we provide a practical example of active learning using a higher-order cognitive model. This is accompanied by a computational study that underscores the unique behaviors that this model produces.

翻译：构建能够高效与人类协作的机器一直是人工智能领域的长期目标。尤其是在存在不确定性的情况下，最优协作往往要求人类与人工智能体相互建模对方的行为，并利用这些模型推断潜在的目标、信念或意图，其中可能涉及多个递归层次。认知科学、语言学和机器人学领域的先前研究也为人类行为中此类高阶认知的存在提供了实证证据。我们倡导一种新的主动学习范式，通过人类反馈来利用人类作为主动数据源，同时考虑其更高层次的能动性。具体而言，我们探讨了能动性层次的提升如何导致主动学习系统与教师之间产生性质不同的理性沟通形式。此外，我们提供了一个使用高阶认知模型的主动学习实践案例，并辅以一项计算研究，该研究突出了该模型所产生的独特行为特征。