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Trust-sensitive belief revision

Hunter, Aaron and Booth, Richard ORCID: 2015. Trust-sensitive belief revision. Presented at: 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, 25-31 July 2015. Published in: Yang, Quiang and Wooldridge, Michael eds. IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence. ACM, pp. 3062-3068.

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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 Fermé 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. When multiple reporting agents are involved, we use a distance function over states to represent differing degrees of trust; this ensures that the most trusted reports will be believed.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: ACM
ISBN: 9781577357384
Date of First Compliant Deposit: 22 April 2016
Date of Acceptance: 16 April 2015
Last Modified: 01 Nov 2022 09:55

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