In this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem $\Pi$, a bound $n$ and a pattern -- defined as an arbitrary sequence of actions -- we encode the problem of finding a plan for $\Pi$ with bound $n$ as a formula with fewer variables and/or clauses than the state-of-the-art rolled-up and relaxed-relaxed-$\exists$ encodings. More importantly, we prove that for any given bound, it is never the case that the latter two encodings allow finding a valid plan while ours does not. On the experimental side, we consider 6 other planning systems -- including the ones which participated in this year's International Planning Competition (IPC) -- and we show that our planner Patty has remarkably good comparative performances on this year's IPC problems.

翻译：本文提出了一种解决线性数值规划问题的新方法，称为符号模式规划。给定规划问题$\Pi$、界限$n$以及一个模式（定义为任意动作序列），我们将寻找$\Pi$在界限$n$内的规划的问题编码为一个公式，该公式的变量和/或子句数量少于现有最优的卷起和松弛-松弛-$\exists$编码。更重要的是，我们证明对于任意给定界限，后两种编码允许找到有效规划而我们的编码不能的情况永远不会发生。在实验方面，我们考虑了6个其他规划系统——包括参加今年国际规划竞赛（IPC）的系统——并证明我们的规划器Patty在今年的IPC问题上具有显著优越的比较性能。