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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |