Belief Merging Operators as Maximum Likelihood Estimators
Authors: Patricia Everaere, Sebastien Konieczny, Pierre Marquis
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we study how the merging operators identified as maximum likelihood estimators for the world swap noise and for the atom swap noise are efficient noise cancellers in practice. This requires to evaluate the number of agents needed for getting a merged base equals to the true state of the world with high probability. To this end, we perform some experiments. We report hereafter the obtained results for the world swap noise and the atom swap noise. |
| Researcher Affiliation | Academia | 1CRISt AL CNRS, Université de Lille, France 2CRIL CNRS, Université d Artois, France 3Institut Universitaire de France, France |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | A full-proof version of the paper is available on http://www.cril.fr/ konieczny/ijcai20proofs.pdf. This link is for a full-proof version of the paper, not source code. |
| Open Datasets | No | In our experiments, the true state of the world ω has been generated by considering every variable v from P in an iterative way, assigning v to 1 with probability 1/2. The paper describes a data generation process, but does not provide access information (link, citation, etc.) for a publicly available dataset. |
| Dataset Splits | No | The paper mentions generating 1000 profiles for increasing values of n, but does not specify explicit training, validation, or test dataset splits in terms of percentages, counts, or predefined splits. |
| Hardware Specification | No | The paper does not provide any details about the specific hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers used in the experiments. |
| Experiment Setup | No | In our experiments, the true state of the world ω has been generated by considering every variable v from P in an iterative way, assigning v to 1 with probability 1/2. We did not assume any integrity constraint to be fulfilled (i.e., µ is supposed to be a valid formula). Then, for several values of the parameter p, the noise model P wsn (resp. P v) has been applied to ω in order to generate 1000 profiles E consisting of n bases for increasing values of n. This describes the data generation process and noise application, but not specific experimental setup details like hyperparameters for a learning model or system-level training settings. |