An Efficient Incremental Simple Temporal Network Data Structure for Temporal Planning

TL;DR

This paper introduces eltaSTN, an efficient data structure for incremental simple temporal networks, significantly improving the performance of temporal planning by reusing computations and reducing memory usage.

Contribution

The paper presents eltaSTN, a novel data structure that enhances incremental updates and efficiency in simple temporal networks for temporal planning.

Findings

eltaSTN outperforms existing methods in speed and memory efficiency.

The data structure effectively supports incremental consistency checks.

Experimental results demonstrate superior performance on temporal planning sequences.

Abstract

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.