Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Graph-Based Factorization of Classical Planning Problems
Authors: Martin Wehrle, Silvan Sievers, Malte Helmert
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 17,012 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,876 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As a proof of concept, we slightly modified the IPC Visitall domain such that moving and marking as visited are represented by different operators. We applied the Fast Downward planner [Helmert, 2006] to this domain and its factorization, using A with strong stubborn sets as well as IDA with sleep sets. Table 1 shows the resulting number of generated nodes (without the last f-layer to avoid tiebreaking issues) and the runtime in seconds. |
| Researcher Affiliation | Academia | University of Basel, Switzerland EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that the source code for their methodology is publicly available, nor does it provide a link. |
| Open Datasets | Yes | As a proof of concept, we slightly modified the IPC Visitall domain such that moving and marking as visited are represented by different operators. |
| Dataset Splits | No | The paper mentions using the 'IPC Visitall domain' but does not specify any training, validation, or test dataset splits, percentages, or methodology for partitioning data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, or memory specifications). |
| Software Dependencies | No | The paper mentions using 'Fast Downward planner [Helmert, 2006]' but does not provide specific version numbers for this software or any other software dependencies. |
| Experiment Setup | No | The paper mentions using IDA with sleep sets and specific heuristics, but it does not provide specific experimental setup details such as hyperparameter values or system-level training settings. |