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..
Armstrong’s Axioms and Navigation Strategies
Authors: Kaya Deuser, Pavel Naumov
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main technical results are soundness and completeness theorems for the logical systems describing properties of navigability with and without perfect recall. |
| Researcher Affiliation | Academia | Kaya Deuser, Pavel Naumov Vassar College 124 Raymond Avenue Poughkeepsie, NY 12604 EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found. The paper primarily presents definitions, lemmas, and theorems for logical systems. |
| Open Source Code | No | No mention or link to open-source code for the methodology described in the paper was found. |
| Open Datasets | No | The paper is theoretical and does not involve training data, datasets, or experiments that would require a public dataset for training. Transition system T0 is used as an example for illustration, not as a dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve training, validation, or test splits for data. It defines logical systems and proves theorems. |
| Hardware Specification | No | No hardware specifications (e.g., specific GPU/CPU models, memory details) were mentioned. The paper is theoretical and does not describe computational experiments that would require specific hardware. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) were listed. The paper focuses on theoretical contributions. |
| Experiment Setup | No | No specific experimental setup details, such as hyperparameters or training configurations, were provided. The paper is theoretical and does not describe computational experiments. |