Booth, Richard  ORCID: https://orcid.org/0000-0002-6647-6381 and Hunter, Aaron
2018.
Trust as a precursor to belief revision.
Journal of Artificial Intelligence Research
61
, pp. 699-722.
10.1613/jair.5521
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Abstract
Belief revision is concerned with incorporating new information into a pre-existing set of beliefs. When the new information comes from another agent, we must first determine if that agent should be trusted. 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 Ferme and Hansson, and we prove a representation result that characterizes the class of trust-sensitive revision operators in terms of a set of postulates. We also show that trust-sensitive revision is manipulable, in the sense that agents can sometimes have incentive to pass on misleading information.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | AI Access Foundation |
| ISSN: | 1076-9757 |
| Related URLs: | |
| Date of First Compliant Deposit: | 3 November 2017 |
| Date of Acceptance: | 5 August 2017 |
| Last Modified: | 17 Nov 2024 16:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/106153 |
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