Dynamic Tree Databases in Automated Planning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909905804328960 |
|---|---|
| author | Joergensen, Oliver Drexler, Dominik Seipp, Jendrik |
| author_facet | Joergensen, Oliver Drexler, Dominik Seipp, Jendrik |
| contents | A central challenge in scaling up explicit state-space search for large tasks is compactly representing the set of generated states. Tree databases, a data structure from model checking, require constant space per generated state in the best case, but they need a large preallocation of memory. We propose a novel dynamic variant of tree databases for compressing state sets over propositional and numeric variables and prove that it maintains the desirable properties of the static counterpart. Our empirical evaluation of state compression techniques for grounded and lifted planning on classical and numeric planning tasks reveals compression ratios of several orders of magnitude, often with negligible runtime overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Tree Databases in Automated Planning Joergensen, Oliver Drexler, Dominik Seipp, Jendrik Artificial Intelligence A central challenge in scaling up explicit state-space search for large tasks is compactly representing the set of generated states. Tree databases, a data structure from model checking, require constant space per generated state in the best case, but they need a large preallocation of memory. We propose a novel dynamic variant of tree databases for compressing state sets over propositional and numeric variables and prove that it maintains the desirable properties of the static counterpart. Our empirical evaluation of state compression techniques for grounded and lifted planning on classical and numeric planning tasks reveals compression ratios of several orders of magnitude, often with negligible runtime overhead. |
| title | Dynamic Tree Databases in Automated Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.12677 |