On the Consistency between Belief Revision and Belief Update
Authors: Theofanis I. Aravanis
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formalize our consistency principle both axiomatically and semantically, and we establish a representation result explicitly connecting the two formalizations. Furthermore, we show that two important concrete types of belief change, namely uniform belief change and parametrized-difference belief change, serve as proof-of-concept examples for the introduced consistency principle, as they fully comply with it. |
| Researcher Affiliation | Academia | Theofanis I. Aravanis EMAIL Department of Mechanical Engineering School of Engineering University of the Peloponnese Patras 263 34, Greece |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It defines logical postulates, theorems, and conditions to describe belief change processes. |
| Open Source Code | No | The paper does not provide any concrete access information for source code, nor does it state that code for the described methodology is being released. |
| Open Datasets | No | The paper is theoretical and uses conceptual examples (e.g., 'a room with a table, a magazine and a book' in Example 13) rather than empirical datasets. There is no mention of publicly available or open datasets with access information. |
| Dataset Splits | No | The paper does not conduct experiments with datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental procedures that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on logical formalisms, not software implementations. It does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not describe any experiments or their setup, including hyperparameters or training configurations. |