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.

介绍了基于规划程序的行动新颖性排名概念以及新颖性优化的 GP 算法，采用基于最佳优先搜索 BFS（v） 和其渐进变体 PGP（v）的方法，引入来自行动方案的提高可行性行动，并提出了新的评估函数和结构化程序限制，以扩展搜索范围。经实验证明，新算法 BFS（v）和 PGP（v）在标准通用计划基准测试中优于现有算法。