AI can not only outperform people in many planning tasks, but also teach them how to plan better. All prior work was conducted in fully observable environments, but the real world is only partially observable. To bridge this gap, we developed the first metareasoning algorithm for discovering resource-rational strategies for human planning in partially observable environments. Moreover, we developed an intelligent tutor teaching the automatically discovered strategy by giving people feedback on how they plan in increasingly more difficult problems. We showed that our strategy discovery method is superior to the state-of-the-art and tested our intelligent tutor in a preregistered training experiment with 330 participants. The experiment showed that people's intuitive strategies for planning in partially observable environments are highly suboptimal, but can be substantially improved by training with our intelligent tutor. This suggests our human-centred tutoring approach can successfully boost human planning in complex, partially observable sequential decision problems.

翻译：人工智能不仅能提升人类在许多规划任务中的表现，还能教导人类如何更高效地规划。然而，此前所有研究均基于完全可观测环境，而现实世界往往仅存在部分可观测性。为弥补这一差距，我们首次提出一种元推理算法，用于发现人类在部分可观测环境中的资源理性规划策略。此外，我们开发了一款智能导师系统，通过针对日益复杂的问题中人类规划方式提供反馈，自动教授所发现的策略。实验证明，我们的策略发现方法优于当前最先进技术。我们在一项预注册的培训实验中（330名参与者）测试了该智能导师系统。结果表明，人类在部分可观测环境中的直觉规划策略高度次优，但通过智能导师系统训练可得到显著改善。这提示我们，以人为本的辅导方法能够有效提升人类在复杂、部分可观测的序贯决策问题中的规划能力。