Autonomous discovery and direct instruction are two distinct sources of learning in children but education sciences demonstrate that mixed approaches such as assisted discovery or guided play result in improved skill acquisition. In the field of Artificial Intelligence, these extremes respectively map to autonomous agents learning from their own signals and interactive learning agents fully taught by their teachers. In between should stand teachable autotelic agents (TAA): agents that learn from both internal and teaching signals to benefit from the higher efficiency of assisted discovery. Designing such agents will enable real-world non-expert users to orient the learning trajectories of agents towards their expectations. More fundamentally, this may also be a key step to build agents with human-level intelligence. This paper presents a roadmap towards the design of teachable autonomous agents. Building on developmental psychology and education sciences, we start by identifying key features enabling assisted discovery processes in child-tutor interactions. This leads to the production of a checklist of features that future TAA will need to demonstrate. The checklist allows us to precisely pinpoint the various limitations of current reinforcement learning agents and to identify the promising first steps towards TAA. It also shows the way forward by highlighting key research directions towards the design or autonomous agents that can be taught by ordinary people via natural pedagogy.

翻译：自主发现与直接指令是儿童学习中的两种不同来源，但教育科学表明，辅助发现或引导游戏等混合方法能更有效地提升技能习得。在人工智能领域，这两类极端分别对应着从自身信号学习的自主智能体以及完全由教师指导的交互式学习智能体。介于两者之间的应是可教学的自主智能体（TAA）：它们既能从内部信号中学习，也能从教学信号中学习，从而受益于辅助发现的更高效率。设计此类智能体将能使现实中的非专家用户引导智能体的学习轨迹，使之符合其期望。更根本的是，这或许也是构建具备人类水平智能的智能体的关键一步。本文提出了一条设计可教学自主智能体的路线图。基于发展心理学与教育科学，我们首先识别出儿童-导师互动中支持辅助发现过程的关键特征，由此生成了未来TAA需展示的特征清单。该清单使我们能够精准定位当前强化学习智能体的诸多局限性，并识别出迈向TAA的初步可行步骤。同时，通过强调设计可供普通人通过自然教学法指导的自主智能体的关键研究方向，本文也指明了前进方向。