Merging or Computing Saturated Cost Partitionings? A Merge Strategy for the Merge-and-Shrink Framework

Silvan Sievers, Thomas Keller, Gabriele Röger

Keywords: classical planning, merge-and-shrink framework

TL;DR: We present a new state-of-the-art merge strategy.

Abstract:

The merge-and-shrink framework is a powerful tool for computing
abstraction heuristics for optimal classical planning. Merging is one
of its name-giving transformations. It entails computing the product
of two factors of a factored transition system. To decide which two
factors to merge, the framework uses a merge strategy. While there
exist many merge strategies, it is generally unclear what constitutes
a strong merge strategy, and a previous analysis shows that there is
still lots of room for improvement with existing merge strategies. In
this paper, we devise a new scoring function for score-based merge
strategies based on answering the question whether merging two
factors has any benefits over computing saturated cost partitioning
heuristics over the factors instead. Our experimental evaluation
shows that our new merge strategy achieves state-of-the-art
performance on IPC benchmarks.

Category: Short

Student: No

Submission Number: 374