A Modular, Multi-Expert Paradigm for Adaptive AI Agents: Toward On-Demand Knowledge Integration and Continual Learning

The integration of domain-specific knowledge into artificial intelligence (AI) agents often requires retraining large-scale models, constraining their adaptability and efficiency. We propose a modular, multi-expert paradigm inspired by biological cognition and the "society of mind" concept. In this framework, an AI agent—such as a humanoid robot or Android—hosts a collection of specialized expert modules. Each module encodes domain-specific competencies and experiences, which can be dynamically retrieved, adapted, or combined to respond to novel tasks. By supporting modular integration of experience "packets," the agent can rapidly assemble, refine, or augment its capabilities. We illustrate how digital twin simulations accelerate the acquisition and encapsulation of new competencies. This approach supports continuous learning, efficient reuse of previously acquired experience, and seamless transfer of specialized knowledge across heterogeneous domains.> Portions of this manuscript were drafted and refined with the assistance of OpenAI's GPT-o1 language model (accessed December 2024). All scientific claims and editorial decisions are the sole responsibility of the author.