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..
Dynamic Logic for Data-aware Systems: Decidability Results
Authors: Francesco Belardinelli, Andreas Herzig
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Most importantly, we develop an abstraction-based verification procedure, thus proving that the model checking problem for Da S against FO-DL is decidable, provided some mild assumptions on the interpretation domain. |
| Researcher Affiliation | Academia | Francesco Belardinelli Laboratoire IBISC, UEVE IRIT Toulouse EMAIL Andreas Herzig IRIT Toulouse Universit e de Toulouse CNRS EMAIL |
| Pseudocode | Yes | tmp := a; a := b; b := tmp;...α = max := a0; if max < a1 then max := a1; ... if max < an then max := an; |
| Open Source Code | No | The paper does not provide any statements about open-source code availability or links to code repositories for the described methodology. |
| Open Datasets | No | The paper describes theoretical work and does not use datasets in the context of empirical training or evaluation. The interpretation domain U=Q (rational numbers) is a theoretical construct, not a publicly available dataset for empirical evaluation. |
| Dataset Splits | No | The paper describes theoretical work and does not refer to empirical dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not include details about an experimental setup, hyperparameters, or training configurations. |