Entrenchment-Based Horn Contraction
Authors: Z. Zhuang, M. Pagnucco
JAIR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we contribute to this line of research by investigating the Horn version of the AGM entrenchment-based contraction. The study is challenging as the construction of entrenchment-based contraction refers to arbitrary disjunctions which are not expressible under Horn logic. In order to adapt the construction to Horn logic, we make use of a Horn approximation technique called Horn strengthening. We provide a representation theorem for the newly constructed contraction which we refer to as entrenchment-based Horn contraction. |
| Researcher Affiliation | Academia | Zhiqiang Zhuang EMAIL Institute for Integrated and Intelligent Systems Griļ¬th University, QLD 4111, Australia; Maurice Pagnucco EMAIL School of Computer Science and Engineering The University of New South Wales, NSW 2052, Australia |
| Pseudocode | No | The paper primarily focuses on theoretical developments, definitions, theorems, and proofs related to entrenchment-based Horn contraction. It does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | This paper is theoretical in nature and does not describe or use any datasets for empirical evaluation. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets, therefore, there is no mention of dataset splits. |
| Hardware Specification | No | The paper describes theoretical work in belief change and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper focuses on theoretical concepts and logical frameworks; it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and presents logical definitions and theorems. It does not describe any experimental setups, hyperparameters, or training configurations. |