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.

人工智能系统的自主和通用行为是可能的，但需要理解和应用关键的结构化知识表示方法，通过交互对象实现类型空间以学习可转移的互动概念和泛化的元认知普适框架 (KIX) 可以与强化学习自然地整合知识，扮演推动人工智能系统实现自主和通用行为的关键角色。