Novelty and Lifted Helpful Actions in Generalized Planning
This work addresses scalability issues in generalized planning, an incremental improvement for AI planning researchers.
The authors tackled the problem of scaling up generalized planning by introducing action novelty rank and lifted helpful actions, resulting in new algorithms BFS(v) and PGP(v) that outperform state-of-the-art methods on standard benchmarks.
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.