Linear-space best-first search

摘要

Best-first search is a general heuristic search algorithm that always expands next a frontier node of lowest cost. It includes as special cases breadth-first search, Dijkstra's single-source shortest-path algorithm, and the A∗ algorithm. Its applicability, however, is limited by its exponential memory requirement. Previous approaches to this problem, such as iterative deepening, do not expand nodes in best-first order if the cost function can decrease along a path. We present a linear-space best-first search algorithm (RBFS) that always explores new nodes in best-first order, regardless of the cost function, and expands fewer nodes than iterative deepening with a nondecreasing cost function. On the sliding-tile puzzles, RBFS with a nonmonotonic weighted evaluation function dramatically reduces computation time with only a small penalty in solution cost. In general, RBFS reduces the space complexity of best-first search from exponential to linear, at the cost of only a constant factor in time complexity in our experiments.