Belief Revision Games

Authors: Nicolas Schwind, Katsumi Inoue, Gauvain Bourgne, Sébastien Konieczny, Pierre Marquis

AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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.