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..
Trust-Sensitive Belief Revision
Authors: Aaron Hunter, Richard Booth
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we deο¬ne 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 EMAIL Richard Booth Mahasarakham University Mahasarakham, Thailand EMAIL |
| 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. |