Jorg Hoffmann and Hector Geffner
Graphplan can be understood as a heuristic search planner that performs an IDA* regression search with a heuristic function encoded in the plan graph. In this paper we study two alternatives to Graphplan where the IDA* search algorithm and the heuristic encoded in the plan graph are maintained but the branching scheme is changed: rather than constructing plans from the tail, commitments are allowed anywhere in the plan. These commitments force certain actions in or out of certain time steps. While the regression search allows Graphplan to build the plan graph only once, in the new branching scheme the plan graph must be computed anew for each state. This re-computation is expensive but is often compensated by a reduced number of states in the search. We show through a number of experiments that the resulting planner scales up much better than the original Graphplan in domains involving a higher degree of parallelism, and compares well to state-of-the-art domain-independent optimal parallel planners.