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 [1].
A* Search and Bound-Sensitive Heuristics for Oversubscription Planning
Authors: Michael Katz, Emil Keyder9813-9820
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented our approach1 in the Fast Downward planner (Helmert 2006), and evaluated it on a set of publicly available OSP benchmarks (Katz et al. 2019b). ... The per-domain and overall coverage, as well as per-task node expansions comparing the two blind search approaches are shown in Table 1 and Figure 2, respectively. ... Domain-level and overall coverage comparison, as well as per-task node expansions for the various configurations and problem suites, are shown in Table 2 and Figure 3, respectively. |
| Researcher Affiliation | Industry | Michael Katz1 Emil Keyder2 1IBM Research, Yorktown Heights, NY, USA 2Invitae Corporation, San Francisco, CA, USA |
| Pseudocode | No | The paper describes algorithms mathematically and conceptually but does not include any explicit pseudocode blocks or sections labeled 'Algorithm'. |
| Open Source Code | Yes | Code available at https://github.com/emilkeyder/fd-2018-osp. |
| Open Datasets | Yes | evaluated it on a set of publicly available OSP benchmarks (Katz et al. 2019b). (Katz, M.; Keyder, E.; Pommerening, F.; and Winterer, D. 2019b. PDDL benchmarks for oversubscription planning. https://doi.org/10.5281/zenodo.2576024.) |
| Dataset Splits | No | The paper does not specify exact percentages, absolute sample counts, or refer to predefined splits for training, validation, and test datasets. It mentions using 'publicly available OSP benchmarks' but does not detail how these were split for the experiments. |
| Hardware Specification | Yes | The experiments were performed on Intel(R) Xeon(R) CPU E7-8837 @2.67GHz machines, with time and memory limits of 30min and 3.5GB. |
| Software Dependencies | No | The paper mentions 'Fast Downward planner (Helmert 2006)' as the implementation base but does not provide specific version numbers for other key software dependencies or libraries. |
| Experiment Setup | Yes | For h MS, we use exact bisimulation with an abstract state space threshold of 50k states and exact generalized label reduction (Sievers, Wehrle, and Helmert 2014), limiting the time for abstraction creation to 600 seconds. |