Heuristic Search

Heuristic search is a family of algorithms designed to efficiently find near-optimal solutions to complex problems by intelligently exploring a search space using guiding heuristics. Current research emphasizes improving the efficiency and robustness of these algorithms, focusing on areas like multi-objective optimization, incorporating large language models for heuristic generation, and developing novel architectures such as multi-queue search and learned pruning methods to reduce computational costs. These advancements are significant for various applications, including robotics (pathfinding, task and motion planning), operations research (scheduling, resource allocation), and artificial intelligence (planning under uncertainty, combinatorial optimization), where efficient solution finding is crucial.