On Definite Iterated Belief Revision with Belief Algebras
Authors: Hua Meng, Zhiguo Long, Michael Sioutis, Zhengchun Zhou
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a novel framework for iterated belief revision by characterizing belief information through preference relations. Semantically, both beliefs and new evidence are represented as belief algebras, which provide a rich and expressive foundation for belief revision. Building on traditional revision rules, we introduce additional postulates for revision with belief algebra, including an upper-bound constraint on the outcomes of revision. We prove that the revision result is uniquely determined given the current belief state and new evidence. Furthermore, to make the framework more useful in practice, we develop a particular algorithm for performing the proposed revision process. |
| Researcher Affiliation | Academia | 1School of Mathematics, Southwest Jiaotong University, China 2School of Computing and Artificial Intelligence, Southwest Jiaotong University, China 3LIRMM UMR 5506, University of Montpellier & CNRS, France EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Definite revision on BAL. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. It only mentions developing an algorithm and discusses testing its application in future work, implying no current public release of code. |
| Open Datasets | No | The paper is theoretical and focuses on a novel framework for belief revision using belief algebras. It does not use any concrete datasets for experimental evaluation, nor does it refer to any publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation using datasets. Therefore, there is no mention of dataset splits. |
| Hardware Specification | No | The paper describes a theoretical framework and an algorithm but does not report on any experimental results or computational performance that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes a theoretical framework and an algorithm. It does not mention any specific software, programming languages, libraries, or their versions used for implementation or experimentation. |
| Experiment Setup | No | The paper focuses on a theoretical framework for belief revision and proposes an algorithm. It does not describe any experiments, and therefore, no experimental setup details, hyperparameters, or training configurations are provided. |