Abstract
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.
Keywords
- Planning and Scheduling: Planning Algorithms
- Planning and Scheduling: Search in Planning and Scheduling
- Heuristic Search and Game Playing: Heuristic Search
