Sampling-Based Belief Revision
Authors: Michael Thielscher
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we examine the logical foundations of sampling-based belief revision. We show that it satisfies six of the standard AGM postulates but not Vacuity nor Subexpansion. We provide a corresponding representation theorem that generalises the standard result from a single to a family of faithful assignments for a given belief set. We also provide a formal axiomatisation of sampling-based belief revision in the Situation Calculus as an alternative way of reasoning about actions, sensing, and beliefs. |
| Researcher Affiliation | Academia | Michael Thielscher University of New South Wales, Australia mit@unsw.edu.au ... The author is also affiliated with the University of Western Sydney. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | The paper describes theoretical work and does not use or refer to any publicly available or open datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper describes theoretical work and does not report on experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper describes theoretical work and does not report on experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not report on experiments, thus no specific experimental setup details (like hyperparameters or training configurations) are provided. |