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Trust as a precursor to belief revision

Booth, Richard ORCID: 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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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: 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: 13 Nov 2023 08:37

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