Tunneling and Decomposition-Based State Reduction for Optimal Planning

摘要

Action pruning is one of the most basic techniques for improving a planner's performance. The challenge of preserving op-timality while reducing the state space has been addressed by several methods in recent years. In this paper we describe two optimality preserving pruning methods: The first is a generalization of tunnel macros. The second, the main contribution of this paper, is a novel partition-based pruning method. The latter requires the introduction of new automated domain decomposition techniques which are of independent interest. Both methods prune the actions applicable at state s based on the last action leading to s, and both attempt to capture the intuition that, when possible, we should focus on one sub-goal at a time. As we demonstrate, neither method dominates the other, and a combination of both allows us to obtain an even stronger pruning rule. We also introduce a few modifications to A* that utilize properties shared by both methods to find an optimal plan. Our empirical evaluation compares the pruning power of the two methods and their combination, showing good coverage, reduction in running time, and reduction in the number of expansions.