Trust-Sensitive Belief Revision
Authors: Aaron Hunter, Richard Booth
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we define trust as a pre-processing step before revision. We emphasize that trust in an agent is often restricted to a particular domain of expertise. We demonstrate that this form of trust can be captured by associating a state partition with each agent, then relativizing all reports to this partition before revising. We position the resulting family of trust-sensitive revision operators within the class of selective revision operators of Ferm e and Hansson, and we examine its properties. In particular, we show how trust-sensitive revision is manipulable, in the sense that agents can sometimes have incentive to pass on misleading information. |
| Researcher Affiliation | Academia | Aaron Hunter British Columbia Institute of Technology Burnaby, Canada aaron hunter@bcit.ca Richard Booth Mahasarakham University Mahasarakham, Thailand ribooth@gmail.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any public datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe implemented systems or experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve an experimental setup with hyperparameters or training settings. |