Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Revisiting Dominance Pruning in Decoupled Search
Authors: Daniel Gnad11809-11817
AAAI 2021 | Venue PDF | 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 EMAIL |
| 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). |