Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning
Abstract
Pattern databases are the foundation of some of the
strongest admissible heuristics for optimal classical
planning. Experiments showed that the most informative way of combining information from multiple pattern databases is to use saturated cost partitioning. Previous work selected patterns and computed saturated cost partitionings over the resulting
pattern database heuristics in two separate steps.
We introduce a new method that uses saturated cost
partitioning to select patterns and show that it outperforms all existing pattern selection algorithms