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 [1].
Finite Based Contraction and Expansion via Models
Authors: Ricardo Guimarães, Ana Ozaki, Jandson S. Ribeiro
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a new paradigm for Belief Change in which the new information is represented as sets of models, while the agent s body of knowledge is represented as a finite set of formulae, that is, a finite base. ... In this setting, we define new Belief Change operations akin to traditional expansion and contraction, and we identify the rationality postulates that emerge due to the finite representability requirement. We also analyse different logics concerning compatibility with our framework. ... In Section 5, we analyse different logics and the ability to define model change operations following our paradigm. |
| Researcher Affiliation | Academia | Ricardo Guimar aes1, Ana Ozaki1, Jandson S. Ribeiro2 1 University of Bergen 2 University of Hagen |
| Pseudocode | No | The paper includes definitions, theorems, and propositions, but no pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training or the use of datasets. Therefore, it does not provide information about public dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. Therefore, it does not provide information about training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. Thus, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments. Therefore, it does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve experimental setups or hyperparameters. Therefore, it does not provide details on such configurations. |