KIX: A Knowledge and Interaction-Centric Metacognitive Framework for Task Generalization

2024-02-08

AI Summary

Current AI agents lack task generalization and contextual adaptability, hindering the reuse of long-term acquired high-level knowledge. Method: This paper proposes the Knowledge–Interaction–Execution (KIX) metacognitive framework—the first to integrate type-space-driven interactive concept learning with metacognitive control. It models interaction relationships via object type spaces, enabling structured knowledge to be naturally injected into reinforcement learning; it further introduces interactive concept abstraction and binding mechanisms to support cross-task knowledge transfer and reuse. Contribution/Results: Experiments demonstrate that KIX significantly improves zero-shot and few-shot generalization performance on unseen tasks. By unifying conceptual abstraction, structural knowledge representation, and adaptive control, the framework establishes a novel paradigm and architectural foundation for scalable, self-adaptive artificial general intelligence systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

People 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 like specialists, 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 is promising to act as an enabler for autonomous and generalist behaviors in artificial intelligence systems.

Problem

• Bridging gap between human and artificial general intelligence
• Enhancing transferable interaction concepts for task generalization
• Integrating knowledge into reinforcement learning for AI adaptability

Innovation

• Metacognitive framework for task generalization
• Leveraging type space for transferable concepts
• Integrating knowledge into reinforcement learning