Rune M. Jensen, Eric A. Hansen, Simon Richards, Rong Zhou
A promising approach to solving large state-space search problems is to integrate heuristic search with symbolic search. Recent work shows that a symbolic A* search algorithm that uses binary decision diagrams to compactly represent sets of states outperforms traditional A* in many domains. Since the memory requirements of A* limit its scalability, we show how to integrate symbolic search with a memory-efficient strategy for heuristic search. We analyze the resulting search algorithm, consider the factors that affect its behavior, and evaluate its performance in solving benchmark problems that include STRIPS planning problems.