  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.
