We establish a novel relation between delete-free planning, an important task for the AI planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans, with our diagnostic encoding outperforming state-of-the-art approaches to relaxed planning regardless of the given time limit when measured on a wide collection of STRIPS planning benchmarks.

我们在免删除规划（AI 规划社区的一项重要任务，也称为宽松规划）和逻辑编程之间建立了一种新颖的关系。我们表明，给定一个规划问题，可以通过描述相应的宽松规划问题的逻辑程序的稳定模型来双射捕获可以命令为该问题产生宽松计划的所有动作子集。我们还考虑逻辑程序支持的模型语义，并将宽松规划问题的一种因果编码和一种诊断编码引入为逻辑程序，两者都用其支持的模型捕获宽松计划。我们的实验结果表明，这些新编码在计算最佳宽松计划时可以提供主要的性能增益，