# Dynamic Tree Databases in Automated Planning

arXiv: https://arxiv.org/abs/2511.12677 · HTML: https://mlsanity.com/paper/2511.12677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 16 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft Worlds](https://mlsanity.com/paper/2312.12891/) (2023)
- [Optimal Layout-Aware CNOT Circuit Synthesis with Qubit Permutation](https://mlsanity.com/paper/2408.04349/) (2024)
- [Dynamic Path-Decomposed Tries](https://mlsanity.com/paper/1906.06015/) (2019)

## Abstract (arXiv)

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.