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].
Efficient Representations for the Modal Logic S5
Authors: Alexandre Niveau, Bruno Zanuttini
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare all three languages from the complexity-theoretic viewpoint of knowledge compilation and also through experiments. Our work sheds light on the pros and cons of each representation in both theory and practice. |
| Researcher Affiliation | Academia | Alexandre Niveau and Bruno Zanuttini GREYC, UMR 6072, UNICAEN/CNRS/ENSICAEN, France {alexandre.niveau,EMAIL} |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes using 'randomly drawn scenarios inspired from planning' and 'random (uniform, satisfiable) term of a given size t' for experiments. This indicates synthetic data generation rather than using a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper describes running experiments with synthetic data, but it does not specify explicit training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We ran experiments with a moderate and a larger number of variables (n = 15 and n = 30; recall that there are 22n structures over n atoms!) with term sizes t = 1,3,7, and numbers of actions m = 1,...,18. For each tuple (n,t,m), we averaged the results over 100 runs. |