Faster Optimal Planning with Partial-Order Pruning

When planning problems have many kinds of resources or high concurrency, each optimal state has exponentially many minor variants, some of which are "better" than others. Standard methods like \Astar cannot effectively exploit these minor relative differences, and therefore must explore many redundant, clearly suboptimal plans. We describe a new optimal search algorithm for planning that leverages a partial order relation between states. Under suitable conditions, states that are dominated by other states with respect to this order can be pruned while provably maintaining optimality. We also describe a simple method for automatically discovering compatible partial orders in both serial and concurrent domains. In our experiments we find that more than 98% of search states can be pruned in some domains.