A deep reinforcement learning-enhanced multi-agent system for ontology-based health management in nanotechnology

Resumen

This study provides a novel approach to the field of prognostics and health management (PHM) in nanotechnology: multi-agent systems integrated with ontology-based knowledge representation and Deep Reinforcement Learning (DRL). This framework has agents acting in a network-like manner, where every agent investigates a particular subject of nanotechnology design and lifecycle management for an interdependent and multifaceted problem-solving approach. Ontologies give the framework a semantic dimension, which allows for the precise and context-dependent interpretation of data. These permit observations attuned towards understanding the behaviors of nanomaterials, performance limitations, and failure mechanisms. On the other hand, having a DRL-integrated module permits agents to provide dynamic adaptation to changing operational contexts, datasets, and user scenarios while continuously calibrating their decisions for better accuracy and efficiency. Preliminary evaluations based on expert-reviewed test cases demonstrated a 95% task success rate and a decision-making accuracy of 96%, indicating the system's strong potential in handling complex nanotechnology scenarios. These results show good robustness and adaptability to certain PHM problems, such as predictive maintenance of nanodevices, lifespan optimization of nanomaterials, and risk assessment in complex environments. This study introduces a novel integration of Multi-Agent Systems (MAS), ontology-driven reasoning, and DRL, enabling dynamic cross-ontology collaboration and online learning capabilities. These features allow the system to adapt to evolving user needs and heterogeneous knowledge domains in nanotechnology.