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..
Belief Revision Games
Authors: Nicolas Schwind, Katsumi Inoue, Gauvain Bourgne, Sébastien Konieczny, Pierre Marquis
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted a number of tests when four propositional symbols are considered in the language LP, for various graph topologies up to 10 agents and for k {1, . . . , 6}. All the tested instances supported the claim. |
| Researcher Affiliation | Academia | Nicolas Schwind Transdisciplinary Research Integration Center National Institute of Informatics Tokyo, Japan; Katsumi Inoue National Institute of Informatics The Graduate University for Advanced Studies Tokyo, Japan; Gauvain Bourgne CNRS & Sorbonne Universit es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France; S ebastien Konieczny CRIL CNRS Universit e d Artois Lens, France; Pierre Marquis CRIL CNRS Universit e d Artois Lens, France |
| Pseudocode | No | The paper provides formal definitions and propositions but does not include pseudocode or algorithm blocks. |
| Open Source Code | Yes | Additionally, we developed a software available online at http://www.cril.fr/brg/brg.jar. It consists of graphical interface which allows one to play BRGs considering any of the 18 revision policies from {Rk | k {1, . . . , 6}, { d D,Σ, d H,Σ, d H,Gmin}}. |
| Open Datasets | No | The paper uses a theoretical propositional language and illustrative examples, not named or publicly accessible datasets. |
| Dataset Splits | No | The paper does not specify dataset splits (training, validation, test) as it focuses on theoretical analysis and illustrative examples rather than empirical evaluation on a dataset. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions a '.jar' software but does not specify any software dependencies with version numbers (e.g., Java version, specific libraries). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters, batch sizes, or training configurations. |