This paper studies the possibilities made open by the use of Lazy Clause Generation (LCG) based approaches to Constraint Programming (CP) for tackling sequential classical planning. We propose a novel CP model based on seminal ideas on so-called lifted causal encodings for planning as satisfiability, that does not require grounding, as choosing groundings for functions and action schemas becomes an integral part of the problem of designing valid plans. This encoding does not require encoding frame axioms, and does not explicitly represent states as decision variables for every plan step. We also present a propagator procedure that illustrates the possibilities of LCG to widen the kind of inference methods considered to be feasible in planning as (iterated) CSP solving. We test encodings and propagators over classic IPC and recently proposed benchmarks for lifted planning, and report that for planning problem instances requiring fewer plan steps our methods compare very well with the state-of-the-art in optimal sequential planning.

翻译：本文研究了基于惰性子句生成（LCG）的约束规划（CP）方法在解决序列经典规划问题中所开辟的可能性。我们提出了一种新颖的CP模型，其基础是将规划作为可满足性问题时所谓提升式因果编码的开创性思想，该模型不需要实例化，因为函数和动作模式的实例化选择成为设计有效规划问题的有机组成部分。该编码不需要对框架公理进行编码，也不显式地将状态表示为每个规划步骤的决策变量。我们还提出了一种传播器过程，展示了LCG扩展推理方法的可能性，使得这些方法在（迭代的）CSP求解中被称为可行。我们在经典的IPC基准和最近提出的提升规划基准上对编码和传播器进行了测试，并报告表明，在需要较少规划步骤的规划问题实例中，我们的方法与最优序列规划领域的最新技术水平相比具有很好的竞争力。