Editing Boolean Classifiers: A Belief Change Perspective
Authors: Nicolas Schwind, Katsumi Inoue, Pierre Marquis
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main goal is to delineate what are the rational ways of making such edits. This goes through a number of rationality postulates inspired from those considered so far for belief revision. We give a representation theorem and present some families of edit operators satisfying the postulates. |
| Researcher Affiliation | Academia | Nicolas Schwind1, Katsumi Inoue2,3, Pierre Marquis4,5 1 National Institute of Advanced Industrial Science and Technology, Tokyo, Japan 2 National Institute of Informatics, Tokyo, Japan 3 The Graduate University for Advanced Studies, SOKENDAI, Tokyo, Japan 4 Univ. Artois, CNRS, CRIL, F-62300 Lens, France 5 Institut Universitaire de France |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. The methods are described mathematically and axiomatically. |
| Open Source Code | No | The paper states: "The proofs of propositions are available online.1 https://nicolas-schwind.github.io/SIM-AAAI23-proofs.pdf" This link is for proofs, not for the source code of the methodology described in the paper. There is no other statement about the availability of the research's source code. |
| Open Datasets | No | The paper is theoretical and uses illustrative examples (e.g., "Example 1. Let us formalize the scenario provided in the introduction. We set PS = {p, q, r, s} where p means that the applicant has a high income...") rather than public datasets for training or evaluation. No concrete access information for a public dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not perform empirical experiments with datasets. Therefore, it does not provide information about training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and focuses on abstract concepts and operators. It does not mention any hardware used for computation or experiments. |
| Software Dependencies | No | The paper is theoretical and describes abstract operators and postulates. It does not mention any specific software, libraries, or solvers with version numbers that would be needed to replicate experimental results. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Therefore, it does not include details about an experimental setup, hyperparameters, or system-level training settings. |