  We investigate learning heuristics for domain-specific planning. Prior work
framed learning a heuristic as an ordinary regression problem. However, in a
greedy best-first search, the ordering of states induced by a heuristic is more
indicative of the resulting planner's performance than mean squared error.
Thus, we instead frame learning a heuristic as a learning to rank problem which
we solve using a RankSVM formulation. Additionally, we introduce new methods
for computing features that capture temporal interactions in an approximate
plan. Our experiments on recent International Planning Competition problems
show that the RankSVM learned heuristics outperform both the original
heuristics and heuristics learned through ordinary regression.
