Searching for Plans with Carefully Designed Probes

We define a probe to be a single action sequence computed greedily from a given state that either terminates in the goal or fails. We show that by designing these probes carefully using a number of existing and new polynomial techniques such as helpful actions, landmarks, and consistent subgoals, a single probe from the initial state can solve by itself 683 out of 980 problems from previous IPCs, a number that compares well with the 627 problems solved by FF in EHC mode, with similar times and plan lengths. We also show that by launching one probe as a lookahead mechanism from each expanded state in a standard greedy best first search informed by the additive heuristic, the number of problems solved jumps to 900 (92%), as opposed to FF that solves 827 problems (84%), and LAMA that solves 844 (86%). The success of probes, like the improvements of FF and LAMA over HSP before, suggests that effective heuristic search planning is more than heuristic search with automatically derived heuristic estimators, with structural forms of inference playing an important