---
title: "Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning"
authors:
  - "Jendrik Seipp"
doi: "10.24963/ijcai.2019/780"
url: "https://ijcai.org/proceedings/2019/780"
pdf: "https://ijcai.org/proceedings/2019/0780.pdf"
venue: "Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"
year: 2019
pages: "5621-5627"
track: "Main track"
keywords:
  - "Planning and Scheduling: Planning and Scheduling"
  - "Planning and Scheduling: Search in Planning and Scheduling"
---

# Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning

Jendrik Seipp

*Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence*, pages 5621-5627. DOI: [10.24963/ijcai.2019/780](https://doi.org/10.24963/ijcai.2019/780)

## 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.