Hunter, A., & Booth, R. (2015). Trust-sensitive belief revision. Proceedings Of The Twenty-Fourth International Joint Conference On Artificial Intelligence, 3062-3068. Retrieved from https://www.ijcai.org/Abstract/15/432
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence in Buenos Aires, Argentina, 25–31 July 2015. 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 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., Conference paper, Published.
