# An Efficient Incremental Simple Temporal Network Data Structure for Temporal Planning

arXiv: https://arxiv.org/abs/2212.07226 · HTML: https://mlsanity.com/paper/2212.07226/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 1 of 1)

- [Temporal Planning with Intermediate Conditions and Effects](https://mlsanity.com/paper/1909.11581/) (2019)

## Abstract (arXiv)

One popular technique to solve temporal planning problems consists in decoupling the causal decisions, demanding them to heuristic search, from temporal decisions, demanding them to a simple temporal network (STN) solver. In this architecture, one needs to check the consistency of a series of STNs that are related one another, therefore having methods to incrementally re-use previous computations and that avoid expensive memory duplication is of paramount importance. In this paper, we describe in detail how STNs are used in temporal planning, we identify a clear interface to support this use-case and we present an efficient data-structure implementing this interface that is both time- and memory-efficient. We show that our data structure, called deltastn, is superior to other state-of-the-art approaches on temporal planning sequences of problems.