IPC Learning Track: Novelty-Based Generalized Planning


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. Besides fact landmarks, other ideas in classical planning have not been introduced to generalized planning, such as novelty-based search. In this paper, we present 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 Progressive Generalized Planning PGP(v). Besides, we introduce new structural program restrictions to scale up the search.

Proceedings International Planning Competition (IPC-10)