It has been shown recently that successful techniques in classical planning, such as goal-oriented heuristics and landmarks, can improve the ability to compute planning programs for generalized planning (GP) problems. In this work, we introduce the notion of action novelty rank, which computes novelty with respect to a planning program, and propose novelty-based generalized planning solvers, which prune a newly generated planning program if its most frequent action repetition is greater than a given bound $v$, implemented by novelty-based best-first search BFS($v$) and its progressive variant PGP($v$). Besides, we introduce lifted helpful actions in GP derived from action schemes, and propose new evaluation functions and structural program restrictions to scale up the search. Our experiments show that the new algorithms BFS($v$) and PGP($v$) outperform the state-of-the-art in GP over the standard generalized planning benchmarks. Practical findings on the above-mentioned methods in generalized planning are briefly discussed.

翻译：论文摘要：近期研究表明，经典规划中的成功技术（如目标导向启发式与地标）可提升泛化规划问题的规划程序计算能力。本文提出动作新颖性秩的概念——该概念基于规划程序计算新颖性，并进一步提出基于新颖性的泛化规划求解器：当新生成的规划程序中最高频动作重复次数超过给定阈值$v$时，通过新颖性驱动的优先搜索BFS($v$)及其渐进变体PGP($v$)进行剪枝。此外，我们引入源自动作模式的泛化规划提升式有用动作，并提出新型评估函数与结构化程序约束以扩展搜索规模。实验表明，新算法BFS($v$)和PGP($v$)在标准泛化规划基准测试中均优于当前最优方法。最后简要讨论了上述方法在泛化规划中的实践发现。