Humans and other animals aptly exhibit general intelligence behaviors in solving a variety of tasks with flexibility and ability to adapt to novel situations by reusing and applying high level knowledge acquired over time. But artificial agents are more of a specialist, lacking such generalist behaviors. Artificial agents will require understanding and exploiting critical structured knowledge representations. We present a metacognitive generalization framework, Knowledge-Interaction-eXecution (KIX), and argue that interactions with objects leveraging type space facilitate the learning of transferable interaction concepts and generalization. It is a natural way of integrating knowledge into reinforcement learning and promising to act as an enabler for autonomous and generalist behaviors in artificial intelligence systems.

翻译：人类及其他动物通过复用和运用随时间积累的高层知识，在解决各类任务时展现出灵活适应新情境的通用智能行为。而人工智能体则更偏向专业化，缺乏此类通用行为模式。人工智能体需要理解并利用关键的结构化知识表征。本文提出一种元认知泛化框架——知识-交互-执行（KIX），并论证借助类型空间与物体进行交互有助于学习可迁移的交互概念及实现泛化。该框架是将知识融入强化学习的自然途径，有望成为实现人工智能系统自主化与通用化行为的关键推手。