Probabilistic Planning

Probabilistic planning addresses the challenge of making optimal decisions in environments with uncertainty, aiming to find plans that maximize the probability of success or achieve desired objectives despite unpredictable events. Current research focuses on developing efficient algorithms, such as Bayesian approaches, Monte Carlo tree search, and semidefinite programming, often integrated with neural networks or other machine learning techniques, to handle complex scenarios with stochastic dynamics and partial observability. These advancements are significantly impacting various fields, including robotics (navigation, manipulation, task planning), autonomous driving, and even disease modeling, by enabling more robust and reliable decision-making in uncertain real-world settings. The development of more efficient and scalable algorithms for multi-objective planning under uncertainty remains a key area of ongoing investigation.