Interactions are central to intelligent reasoning and learning abilities, with the interpretation of abstract knowledge guiding meaningful interaction with objects in the environment. While humans readily adapt to novel situations by leveraging abstract knowledge acquired over time, artificial intelligence systems lack principled mechanisms for incorporating abstract knowledge into learning, leading to fundamental challenges in the emergence of intelligent and adaptive behavior. To address this gap, we introduce knowledge-centric metacognitive learning based on three key principles: natural abstractions, knowledge-guided interactions through interpretation, and the composition of interactions for problem solving. Knowledge learning facilitates the acquisition of abstract knowledge and the association of interactions with knowledge, while object interactions guided by abstract knowledge enable the learning of transferable interaction concepts, abstract reasoning, and generalization. This metacognitive mechanism provides a principled approach for integrating knowledge into reinforcement learning and offers a promising pathway toward intelligent and adaptive behavior in artificial intelligence, robotics, and autonomous systems.

翻译：交互是智能推理与学习能力的核心，其中抽象知识的解释引导着与环境对象的有意义交互。人类能够利用随时间积累的抽象知识轻松适应新情境，而人工智能系统缺乏将抽象知识纳入学习的机制化方法，这导致了智能自适应行为涌现的根本性挑战。为弥补这一不足，我们提出基于三大核心原则的知识中心元认知学习：自然抽象、通过解释实现知识引导的交互，以及面向问题解决的交互组合。知识学习促进抽象知识的获取及交互与知识的关联，而由抽象知识引导的对象交互则支持可迁移交互概念的学习、抽象推理及泛化能力。该元认知机制为将知识整合至强化学习提供了原则性框架，并为人工智能、机器人及自主系统实现智能自适应行为开辟了前景广阔的研究路径。