Germane Conflicts: Desirable Properties for Localising Inconsistency
Authors: Glauber de Bona, Anthony Hunter
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work provides a set of desirable properties to assess definitions for germane conflicts. Also, a new conflict definition, based on substitution, is presented and evaluated via the proposed properties, and the related computational complexity is analysed. |
| Researcher Affiliation | Academia | 1Polytechnic School of the University of S ao Paulo, Av. Prof. Luciano Gualberto 158, S ao Paulo, Brazil 2Department of Computer Science, University College London, Gower Street, London, UK EMAIL, EMAIL |
| Pseudocode | No | The paper describes theoretical concepts, definitions, propositions, and theorems, but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to source code repositories, explicit statements about code release, or mention of code in supplementary materials. |
| Open Datasets | No | The paper uses illustrative examples (Examples 1, 2, 3, 5) to explain concepts but does not refer to any specific publicly available datasets for experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation on datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper describes theoretical contributions and does not report on experimental results that would require specific hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical development and does not describe an implementation, thus no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is primarily theoretical, introducing desirable properties for localizing inconsistency and a new conflict definition, and therefore does not include specific experimental setup details such as hyperparameters or training configurations. |