Tree Search

Tree search algorithms are computational methods that explore decision spaces by building and traversing tree-like structures to find optimal solutions or near-optimal solutions within a given computational budget. Current research focuses on integrating tree search with other powerful techniques, such as reinforcement learning, large language models (LLMs), and graph neural networks, to enhance performance in diverse applications ranging from quantum computing and game playing to program synthesis and robotic navigation. These hybrid approaches aim to overcome limitations of traditional tree search methods, particularly in complex, high-dimensional problems, and are demonstrating significant improvements in efficiency and solution quality across various domains.