Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 4152-4160. https://doi.org/10.24963/ijcai.2020/574
Cost partitioning is a method for admissibly combining admissible heuristics. In this work, we extend this concept to merge-and-shrink (M&S) abstractions that may use labels that do not directly correspond to operators. We investigate how optimal and saturated cost partitioning (SCP) interact with M&S transformations and develop a method to compute SCPs during the computation of M&S. Experiments show that SCP significantly improves M&S on standard planning benchmarks.
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