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流行技术是将因果决定脱钩,要求它们从时间决定到超常搜索,要求它们到简单的时间网络(STN)求解器。在这个结构中,人们需要检查一系列相互关联、相互关联的STN的连贯性,因此,拥有逐步重复使用以往计算方法并避免昂贵的记忆重复至关重要。在本文中,我们详细描述在时间规划中如何使用STN,我们找出支持这一使用案例的明确界面,我们提出一个高效的数据结构来实施这一界面,既具有时间效率,又具有记忆效率。我们显示我们称为“deltastn”的数据结构优于关于时间规划顺序问题的其他最先进的方法。