@inproceedings{9c7bfd93571f4f569ad6b09be05cd347,
title = "Interleaving search and heuristic improvement",
abstract = "Abstraction heuristics are a leading approach for deriving admissible estimates in cost-optimal planning. However, a drawback with respect to other families of heuristics is that they require a preprocessing phase for choosing the abstraction, computing the abstract distances, and/or suitable cost-partitionings. Typically, this is performed in advance by a fixed amount of time, even though some instances could be solved much faster with little or no preprocessing. We interleave the computation of abstraction heuristics with search, avoiding a long precomputation phase and allowing information from the search to be used for guiding the abstraction selection. To evaluate our ideas, we implement them on a planner that uses a single symbolic PDB. Our results show that delaying the preprocessing is not harmful in general even when an important amount of preprocessing is required to obtain good performance.",
keywords = "Artificial Intelligence (AI), Planning and scheduling, Heuristic search",
author = "Santiago Franco and {\'A}lvaro Torralba",
year = "2019",
language = "English",
series = "Proceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019",
pages = "130--134",
editor = "Pavel Surynek and William Yeoh",
booktitle = "Proceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019",
publisher = "AAAI Press",
address = "United States",
note = "12th International Symposium on Combinatorial Search, SoCS 2019 ; Conference date: 16-07-2019 Through 17-07-2019",
}