Large Language Model Driven Interactive Learning for Real-Time Cognitive Load Prediction in Human-Swarm Systems
Wenshuo Zang, Mengsha Hu, Rui Liu · 2024
The rapid advancements of drones have demonstrated the versatility and promising potential of human-swarm systems (HSS) across various domains. However, human performance within these systems may be impaired by factors such as limited domain knowledge and mental stress, often leading to cognitive overload and hindering the efficiency and effectiveness of human-swarm teaming. Consequently, the accurate monitoring of cognitive load levels is crucial for optimizing HSS performance. To address the challenges of existing measurement methods, which are often expensive, time-consuming, or lack real-time capabilities, we propose a Large Language Model driven cognitive load prediction framework. This framework integrates comprehensive task context, domain knowledge, and behavior analysis to provide fast and cost-effective predictions in complex scenarios. By leveraging the capabilities of Large Language Models and employing reinforcement learning to model the cognitive load generation, our framework aims to offer real-time insights into human-related factors causing high cognitive load and predict cognitive levels over time, ultimately enhancing the performance of HSS teaming.