Adaptive Strategy

Adaptive strategies in various fields aim to optimize decision-making and learning processes by dynamically adjusting parameters or actions based on observed data or environmental feedback. Current research focuses on developing algorithms that adapt to uncertainty (e.g., Bayesian adaptive methods, metareasoning frameworks), resource constraints (e.g., efficient model growing strategies), and dynamic environments (e.g., flexible deep Q-networks for real-time decision-making). These advancements have significant implications for improving the efficiency and robustness of AI systems, enhancing human-robot collaboration, and providing more effective management strategies for complex adaptive systems across diverse domains.