Criticality Analysis

Criticality analysis focuses on identifying and quantifying the importance of specific states or actions within complex systems, aiming to optimize performance and safety. Current research explores this concept across diverse fields, employing methods like reinforcement learning (with algorithms such as PPO and A2C), and leveraging techniques such as clustering and criticality-aware fault tolerance to improve efficiency and robustness in applications ranging from autonomous systems to large language models. This analysis is crucial for enhancing the reliability and safety of intelligent systems, particularly in safety-critical domains like nuclear reactor control and autonomous driving, by enabling targeted interventions and resource allocation.