Top Quality Planning

Top-quality planning focuses on generating multiple high-quality solutions to complex decision-making problems, moving beyond finding a single optimal solution. Current research explores diverse approaches, including recursive tree planners that integrate learned policies, transformer-based models that learn search dynamics, and methods that leverage symbolic representations to improve large language model planning capabilities. This research is significant for advancing autonomous systems (like self-driving cars and UAVs) and improving the efficiency and robustness of planning algorithms across various domains, from robotics to game playing.