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.

翻译：解决时间规划问题的一种常用技术是将因果决策（交由启发式搜索处理）与时间决策（交由简单时间网络求解器处理）相解耦。在此架构中，需要检查一系列相互关联的简单时间网络的一致性，因此，开发能够增量重用先前计算且避免昂贵内存复制的算法至关重要。本文详细描述了简单时间网络在时间规划中的应用方式，明确了支持该应用场景的清晰接口，并提出了一种实现该接口的高效数据结构，该结构兼具时间与内存效率。实验表明，相较于其他现有先进方法，我们所提出的数据结构（称为\deltastn）在处理时间规划问题序列时具有更优性能。