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.

翻译：我们在无删除规划（AI规划领域的重要任务，亦称松弛规划）与逻辑程序之间建立了新型关联。研究表明，对于给定规划问题，所有可排序生成松弛规划的动作子集，均可通过描述相应松弛规划问题的逻辑程序的稳定模型实现双射捕获。同时考虑逻辑程序的支持模型语义，我们提出了松弛规划问题的因果性编码与诊断性编码两种逻辑程序表示，二者均能通过其支持模型捕获松弛规划。实验结果表明，这些新编码可在计算最优松弛规划时带来显著的性能提升。在广泛收集的STRIPS规划基准测试中，无论给定时间限制如何，我们的诊断编码均优于当前最优的松弛规划方法。