Hunter, Aaron and Booth, Richard  ORCID: https://orcid.org/0000-0002-6647-6381
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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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 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: | 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 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/89681 |
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