KIX: A Knowledge and Interaction-Centric Metacognitive Framework for Task Generalization
This work addresses the challenge of enabling generalist behaviors in AI, robotics, and autonomous systems, though it appears incremental as it builds on existing knowledge integration approaches.
The paper tackles the problem of artificial agents lacking generalist behaviors by introducing the KIX metacognitive framework, which leverages structured knowledge and interactions to facilitate transferable concepts and promote generalization in reinforcement learning.
People aptly exhibit general intelligence behaviors through flexible problem-solving and the ability to adapt to novel situations by reusing and applying high-level knowledge acquired over time. In contrast, artificial agents tend to be specialists, lacking such generalist behaviors. To bridge this gap, artificial agents will require understanding and exploiting critical structured knowledge representations. We introduce a metacognitive reasoning framework, Knowledge-Interaction-eXecution (KIX), and argue that interactions with objects, by leveraging a type space, facilitate the learning of transferable interaction concepts and promote generalization. This framework offers a principled approach for integrating knowledge into reinforcement learning and holds promise as an enabler for generalist behaviors in artificial intelligence, robotics, and autonomous systems.