When Optimal Is Just Not Good Enough: Learning Fast Informative Action Cost Partitionings

Abstract

Several recent heuristics for domain independent planning adopt some action cost partitioning scheme to derive admissible heuristic estimates. Given a state, two methods for obtaining an action cost partitioning have been proposed: optimal cost partitioning, which results in the best possible heuristic estimate for that state, but requires a substantial computational effort, and ad-hoc (uniform) cost partitioning, which is much faster, but is usually less informative. These two methods represent almost opposite points in the tradeoff between heuristic accuracy and heuristic computation time. One compromise that has been proposed between these two is using an optimal cost partitioning for the initial state to evaluate all states. In this paper, we propose a novel method for deriving a fast, informative cost-partitioning scheme, that is based on computing optimal action cost partitionings for a small set of states, and using these to derive heuristic estimates for all states. Our method provides greater control over the accuracy/computation-time tradeoff, which, as our empirical evaluation shows, can result in better performance.