Name
Abstract | ||
|---|---|---|
Hybrid PDDL+ models are amongst the most advanced models of systems and the resulting problems are notoriously difficult for planners to cope with due to nonlinear behaviours and immense search spaces. This difficulty is exacerbated by the potentially huge size of the fully ground representations that are used by modern planners in order to effectively explore the search space, which can make some problems impossible to tackle, with the result that in several situations the grounding phase has to be done externally or manually. This not only produces a much less compact problem description, but also complicates debugging and model reuse. To overcome the aforementioned limit, in this paper we investigate two simple grounding techniques for PDDL+ problems. The former method we propose extends the simple mechanism of invariance analysis to limit the groundings of operators upfront. The latter proposes to tackle the grounding process by means of a PDDL+ to Classical Planning abstraction. A preliminary experimental analysis over benchmarks coming from real case study shows that not only the grounding can be sped up, but that also problems that were out of the reach before can now be efficiently solved in an automated manner. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICTAI50040.2020.00024 | 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | DocType | ISSN |
Automated Planning,Hybrid PDDL+ Planning,Grounding | Conference | 1082-3409 |
ISBN | Citations | PageRank |
978-1-7281-8536-1 | 0 | 0.34 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrico Scala | 1 | 25 | 12.71 |
| Mauro Vallati | 2 | 216 | 46.63 |