Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality. While most planners ground these representations to simplify reasoning, this can cause an exponential blowup in size. Recent approaches instead operate directly on the lifted level to avoid full grounding. We explore a middle ground between fully lifted and fully grounded planning by introducing three SAT encodings that keep actions lifted while partially grounding predicates. Unlike previous SAT encodings, which scale quadratically with plan length, our approach scales linearly, enabling better performance on longer plans. Empirically, our best encoding outperforms the state of the art in length-optimal planning on hard-to-ground domains.

翻译：经典规划问题通常采用提升的一阶表示进行定义，这种表示兼具紧凑性与普适性。尽管多数规划器通过接地处理简化推理过程，但这可能导致规模呈指数级爆炸增长。近期方法则直接在提升层面操作以避免完全接地。本文通过引入三种SAT编码探索完全提升规划与完全接地规划之间的折中方案：在保持动作提升性的同时，对谓词实施部分接地。与先前随规划长度呈二次扩增的SAT编码不同，我们的方法呈线性扩展特性，从而在长规划任务中实现更优性能。实验表明，在难以接地的领域进行长度最优规划时，我们的最佳编码表现优于当前最优方法。