On Consensus in Belief Merging
Authors: Nicolas Schwind, Pierre Marquis
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We define a consensus postulate in the propositional belief merging setting. The interplay of this new postulate with the IC postulates for belief merging is studied, and an incompatibility result is proved. The maximal sets of IC postulates which are consistent with the consensus postulate are exhibited. When satisfying some of the remaining IC postulates, consensus operators are shown to suffer from a weak inferential power. We then introduce two families of consensus operators having a better inferential power by setting aside some of these postulates. |
| Researcher Affiliation | Academia | Nicolas Schwind National Institute of Advanced Industrial Science and Technology, Tokyo, Japan nicolas-schwind@aist.go.jp Pierre Marquis CRIL-CNRS, Universit e d Artois, Institut Universitaire de France Lens, France marquis@cril.fr |
| Pseudocode | No | The paper contains formal definitions, propositions, and proofs, but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only mentions that "An extended version of the paper containing all the proofs is available from www.cril.fr/ marquis/aaai18-extended.pdf." which refers to the paper itself, not code. |
| Open Datasets | No | The paper uses illustrative examples with propositional symbols and belief bases (e.g., Example 1), but these are constructed for theoretical demonstration and not referred to as publicly available or open datasets for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory, or cloud resources) used for running experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions) that would be needed to replicate any experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not include specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |