Dynamic Tree Databases in Automated Planning

TL;DR

This paper introduces a dynamic tree database structure for efficient state set compression in automated planning, achieving significant memory savings with minimal runtime impact.

Contribution

It presents a novel dynamic variant of tree databases that maintains static properties and improves memory efficiency in planning tasks.

Findings

Achieves compression ratios of several orders of magnitude.

Maintains low runtime overhead during state compression.

Effective for both grounded and lifted planning tasks.

Abstract

A central challenge in scaling up explicit state-space search for large tasks is compactly representing the set of generated states. Tree databases, a data structure from model checking, require constant space per generated state in the best case, but they need a large preallocation of memory. We propose a novel dynamic variant of tree databases for compressing state sets over propositional and numeric variables and prove that it maintains the desirable properties of the static counterpart. Our empirical evaluation of state compression techniques for grounded and lifted planning on classical and numeric planning tasks reveals compression ratios of several orders of magnitude, often with negligible runtime overhead.