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].
Heuristics and Symmetries in Classical Planning
Authors: Alexander Shleyfman, Michael Katz, Malte Helmert, Silvan Sievers, Martin Wehrle
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the symmetry properties of existing heuristics and reveal that many of them are invariant under symmetries. |
| Researcher Affiliation | Collaboration | Alexander Shleyfman Technion, Haifa, Israel EMAIL Michael Katz IBM Haifa Research Lab, Israel EMAIL Malte Helmert and Silvan Sievers and Martin Wehrle University of Basel, Switzerland EMAIL |
| Pseudocode | No | The paper does not contain any sections labeled 'Pseudocode' or 'Algorithm'. It provides mathematical equations for defining heuristics, but these are not formatted as pseudocode blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. Therefore, no information on public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. Therefore, no information on dataset splits for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. It references existing tools like BLISS (Junttila and Kaski 2007) but not as software dependencies for its own work. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |