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].
A Computationally Grounded Logic of 'Seeing-to-it-that'
Authors: Andreas Herzig, Emiliano Lorini, Elise Perrotin
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove its PSPACE-completeness and we show how the concept of social in๏ฌuence can be captured. |
| Researcher Affiliation | Academia | IRIT, CNRS, Toulouse, France 2CRIL, CNRS, Lens, France |
| Pseudocode | No | The paper is theoretical and focuses on logic and complexity proofs. It does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links or statements about releasing source code. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies or datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies or dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on logic and model checking. It does not describe any experimental setup details such as hyperparameters or training settings. |