Jendrik Seipp, Samuel von Allmen, Malte Helmert |
PosterID:
55
PDF
Slides
Poster
BibTeX
|
| Counterexample-guided Cartesian abstraction refinement has been shown to yield informative heuristics for optimal classical planning. The algorithm iteratively finds an abstract solution and uses it to decide how to refine the abstraction. Since the abstraction grows in each step, finding solutions is the main bottleneck of the refinement loop. We cast the refinements as an incremental search problem and show that this drastically reduces the time for computing abstractions. | |
| Canb | 10/28/2020, 21:00 – 21:45 |
| 10/30/2020, 04:00 – 04:45 |
| Paris | 10/28/2020, 11:00 – 11:45 |
| 10/29/2020, 18:00 – 18:45 |
| NYC | 10/28/2020, 06:00 – 06:45 |
| 10/29/2020, 13:00 – 13:45 |
| LA | 10/28/2020, 03:00 – 03:45 |
| 10/29/2020, 10:00 – 10:45 |
A Novel Lookahead Strategy for Delete Relaxation Heuristics in Greedy Best-First Search
Maximilian Fickert
Incremental Search for Counterexample-Guided Cartesian Abstraction Refinement
Jendrik Seipp, Samuel von Allmen, Malte Helmert
Strengthening Potential Heuristics with Mutexes and Disambiguations
Daniel Fišer, Rostislav Horčík, Antonín Komenda