论文标题
有效的渐进时间网络数据结构，用于时间计划
An Efficient Incremental Simple Temporal Network Data Structure for Temporal Planning
论文作者
论文摘要
解决时间计划问题的一种流行技术在于取消因果决策，要求它们从时间决策到启发式搜索，要求它们到简单的时间网络（STN）求解器。在此体系结构中，需要检查一系列相互关联的STN的一致性，因此具有逐步重新使用先前计算的方法，并且避免昂贵的内存重复是至关重要的。在本文中，我们详细描述了如何在时间计划中使用STN，我们确定了一个清晰的接口来支持此用例，并提出了实现该接口的有效数据结构，该接口既具有时间和内存效率效率。我们表明，我们的数据结构称为\ deltastn，优于时间计划问题序列的其他最新方法。
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.
