Revisiting Dominance Pruning in Decoupled Search
Authors: Daniel Gnad11809-11817
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that all our improvements are indeed beneficial in many standard benchmarks. |
| Researcher Affiliation | Academia | Daniel Gnad Saarland University Saarland Informatics Campus Saarbr ucken, Germany gnad@cs.uni-saarland.de |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and experimental data of our evaluation are publicly available (Gnad 2021). doi:10.5281/zenodo.4574401. |
| Open Datasets | Yes | We conducted our experiments using the Lab Python package (Seipp et al. 2017) on all benchmark domains of the International Planning Competition (IPC) from 1998-2018 in both the optimal and satisficing tracks. |
| Dataset Splits | No | The paper mentions using benchmark domains from the International Planning Competition, but it does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | Yes | The experiments were performed on a cluster of Intel E5-2660 machines running at 2.20 GHz with the common runtime/memory limits of 30min/4Gi B. |
| Software Dependencies | No | The paper mentions using the 'decoupled search planner by Gnad & Hoffmann (2018)', 'Fast Downward planning system (Helmert 2006)', and 'Lab Python package (Seipp et al. 2017)', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For optimal planning, we run blind search and A with h LM-cut (Helmert and Domshlak 2009); in satisficing planning, we use greedy best-first search (GBFS) with the h FF heuristic without preferred operator pruning (Hoffmann and Nebel 2001); to prove unsolvability, we run A with the hmax heuristic (Bonet and Geffner 2001). |