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Building Trust for Belief Revision

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PRICAI 2021: Trends in Artificial Intelligence (PRICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13031))

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Abstract

Trust plays a role in the process of belief revision. When information is reported by another agent, it should only be believed if the reporting agent is trusted as an authority over some relevant domain. In practice, an agent will be trusted on a particular topic if they have provided accurate information on that topic in the past. In this paper, we demonstrate how an agent can construct a model of knowledge-based trust based on the accuracy of past reports. We then show how this model of trust can be used in conjunction with Ordinal Conditional Functions to define two approaches to trust-influenced belief revision. In the first approach, strength of trust and strength of belief are assumed to be incomparable as they are on different scales. In the second approach, they are aggregated in a natural manner.

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Correspondence to Aaron Hunter .

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Hunter, A. (2021). Building Trust for Belief Revision. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_40

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  • DOI: https://doi.org/10.1007/978-3-030-89188-6_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89187-9

  • Online ISBN: 978-3-030-89188-6

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