Probabilistic belief revision via imaging
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Nayak, Abhaya
Schwitter, Rolf
Sattar, Abdul
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Pham, DN
Park, SB
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Gold Coast, AUSTRALIA
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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.
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PRICAI 2014: TRENDS IN ARTIFICIAL INTELLIGENCE
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8862
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Artificial intelligence not elsewhere classified