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  • Probabilistic belief revision via imaging

    Author(s)
    Chhogyal, Kinzang
    Nayak, Abhaya
    Schwitter, Rolf
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Year published
    2014
    Metadata
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    Abstract
    While Bayesian conditioning fits in nicely with probabilistic belief expansion, its use is problematic in the context of non-trivial belief revision. Lewis' use of imaging based on closeness between possible worlds offers a way to overcome this limitation in the context of belief update (in a dynamic environment). In this paper, we explore the use of imaging as a means to construct probabilistic belief revision. Specifically, we present explicit constructions of three candidates strategies, dubbed Naive, Gullible and Cunning, that are based on imaging, and investigate their properties.While Bayesian conditioning fits in nicely with probabilistic belief expansion, its use is problematic in the context of non-trivial belief revision. Lewis' use of imaging based on closeness between possible worlds offers a way to overcome this limitation in the context of belief update (in a dynamic environment). In this paper, we explore the use of imaging as a means to construct probabilistic belief revision. Specifically, we present explicit constructions of three candidates strategies, dubbed Naive, Gullible and Cunning, that are based on imaging, and investigate their properties.
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    Conference Title
    PRICAI 2014: TRENDS IN ARTIFICIAL INTELLIGENCE
    Volume
    8862
    Publisher URI
    http://www.pricai2014.org/
    DOI
    https://doi.org/10.1007/978-3-319-13560-1_55
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/67964
    Collection
    • Conference outputs

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