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.
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Chhogyal, K., Nayak, A., Schwitter, R., Sattar, A. (2014). Probabilistic Belief Revision via Imaging. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_55
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DOI: https://doi.org/10.1007/978-3-319-13560-1_55
Publisher Name: Springer, Cham
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