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Width-based Algorithms for Classical Planning: New Results

We have recently shown that classical planning problems can be characterized in terms of a width measure that is bounded and small for most planning benchmark domains when goals are restricted to single atoms. Two simple algorithms have been devised for exploiting this structure: Iterated Width (IW) for achieving atomic goals, that runs in time exponential in the problem width by performing a sequence of pruned breadth first searches, and Serialized IW (SIW) that uses IW in a greedy search for achieving conjunctive goals one goal at a time. While SIW does not use heuristic estimators of any sort, it manages to solve more problems than a Greedy BFS using a heuristic like hadd. Yet, it does not approach the performance of more recent planners like LAMA. In this short paper, we introduce two simple extension to IW and SIW that narrow the performance gap with state-of-the-art planners. The first involves changing the greedy search for achieving the goals one at a time, by a depth-first search that is able to backtrack. The second involves computing a relaxed plan once before going to the next subgoal for making the pruning in the breadth-first procedure less agressive, while keeping IW exponential in the width parameter. The empirical results are interesting as they follow from ideas that are very different from those used in current planners.