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Nearly Counterfactual Revision

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Advances in Artificial Intelligence (Canadian AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9673))

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Abstract

We consider belief revision involving conditional statements where the antecedent is almost certainly false. In order to represent such statements, we use Ordinal Conditional Functions that may take infinite values. In this manner, we are able to capture the intuition that the antecedent can not be verified by a finite number of observations. We define belief revision in this context through basic ordinal arithmetic, and we propose an approach to conditional revision in which only the right hypothetical levels are revised by conditional information. We compare our approach to existing work on conditional revision and belief improvement.

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Authors and Affiliations

  1. British Columbia Institute of Technology, Burnaby, Canada

    Aaron Hunter

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  1. Aaron Hunter
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Correspondence to Aaron Hunter .

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Editors and Affiliations

  1. Lakehead University, Thunder Bay, Ontario, Canada

    Richard Khoury

  2. National Research Council Canada , Ottawa, Canada

    Christopher Drummond

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© 2016 Springer International Publishing Switzerland

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Hunter, A. (2016). Nearly Counterfactual Revision. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_32

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  • DOI: https://doi.org/10.1007/978-3-319-34111-8_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34110-1

  • Online ISBN: 978-3-319-34111-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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