Refinements on the Complementary PDB Construction Mechanism

TL;DR

The paper addresses improving cost-optimal planning via refinements to the Complementary PDB construction mechanism, advancing CPC1 into CPC0. It introduces GAMER-Style enhancements, Next-Fit and CBP bin-packing schemes, and a random-walk evaluator within an adaptive framework that uses a UCB1-based policy to select among strategies. Experiments on IPC 2018 benchmarks show CPC0 delivers higher initial heuristic values, shorter search times, and solves more tasks than CPC1, albeit with higher total time and memory due to extended PDB pre-processing. These results demonstrate that integrated, adaptive PDB construction can significantly boost performance in symbolic planning and set the stage for further improvements such as saturated cost-partitioning and stratified evaluators.

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.

Figures (2)

• Figure 1: The PDB construction process
• Figure 2: Comparison between CPC0 and CPC1 on the planning tasks