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].
Adding Local Exploration to Greedy Best-First Search in Satisficing Planning
Authors: Fan Xie, Martin Müller, Robert Holte
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments were run on a set of 2112 problems in 54 domains from the seven International Planning Competitions which are publicly available |
| Researcher Affiliation | Academia | Fan Xie and Martin Müller and Robert Holte Computing Science, University of Alberta Edmonton, Canada |
| Pseudocode | Yes | Algorithm 1 shows GBFS-LE. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described in this paper. |
| Open Datasets | Yes | Experiments were run on a set of 2112 problems in 54 domains from the seven International Planning Competitions which are publicly available |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., percentages or absolute counts) for reproducing the experiments. |
| Hardware Specification | Yes | Experiments were run on a set of 2112 problems in 54 domains from the seven International Planning Competitions which are publicly available2, using one core of a 2.8 GHz machine with 4 GB memory and 30 minutes per instance. |
| Software Dependencies | Yes | All planners are implemented on the Fast Downward code base FD-2011 (Helmert 2006). |
| Experiment Setup | Yes | Parameters were set as follows: STALL_SIZE = 1000 for both algorithms. (MAX_LOCAL_TRY, LSSIZE) = (100, 1000) for GBFS-LS and (10, 100) for GBFS-LRW. |