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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Consistency between Belief Revision and Belief Update
Authors: Theofanis I. Aravanis
JAIR 2025 | Venue PDF | 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. |