Greedy Best First Search

Greedy Best-First Search (GBFS) is a heuristic search algorithm aiming to efficiently find optimal solutions by prioritizing states with the lowest estimated cost to the goal. Current research focuses on improving GBFS's performance through parallelization techniques, such as decoupling state generation and evaluation, and by developing more effective heuristic functions, including those learned from data using neural networks and optimized for ranking rather than precise cost estimation. These advancements are significant for improving the efficiency of various applications, such as large language model serving and classical planning, where GBFS is used to navigate complex search spaces.