Contingency Planning

Contingency planning focuses on developing proactive strategies to handle unexpected events or failures, aiming to maintain system functionality and achieve desired outcomes despite disruptions. Current research emphasizes robust model architectures, such as Markov Decision Processes (MDPs) and their extensions (e.g., bi-level MDPs, POMDPs), graph neural networks, and various machine learning algorithms (e.g., genetic algorithms, A*, Bayesian methods) to optimize decision-making under uncertainty and multiple objectives. This field is crucial for enhancing the resilience and reliability of complex systems across diverse domains, from autonomous vehicles and robotics to supply chains and space missions, by enabling efficient and safe responses to unforeseen circumstances.