Refinements on the Complementary PDB Construction Mechanism

TL;DR

This paper improves the pattern database construction mechanism of the Complementary 1 (CPC1) planner, leading to significant performance gains in IPC 2018 benchmarks by refining the heuristic generation process.

Contribution

The paper introduces refinements to the PDB construction mechanism of CPC1, enhancing its effectiveness and performance in planning benchmarks.

Findings

Significant performance improvements over the original CPC1.

Enhanced PDB construction leads to better heuristic quality.

Improved planner achieved higher success rates in IPC 2018.

Abstract

Pattern database (PDB) is one of the most popular automated heuristic generation techniques. A PDB maps states in a planning task to abstract states by considering a subset of variables and stores their optimal costs to the abstract goal in a look up table. As the result of the progress made on symbolic search over recent years, symbolic-PDB-based planners achieved impressive results in the International Planning Competition (IPC) 2018. Among them, Complementary 1 (CPC1) tied as the second best planners and the best non-portfolio planners in the cost optimal track, only 2 tasks behind the winner. It uses a combination of different pattern generation algorithms to construct PDBs that are complementary to existing ones. As shown in the post contest experiments, there is room for improvement. In this paper, we would like to present our work on refining the PDB construction mechanism of CPC1. By testing on IPC 2018 benchmarks, the results show that a significant improvement is made on our modified planner over the original version.