Probabilistic belief revision via imaging
Author(s)
Chhogyal, Kinzang
Nayak, Abhaya
Schwitter, Rolf
Sattar, Abdul
Griffith University Author(s)
Year published
2014
Metadata
Show full item recordAbstract
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
Subject
Artificial intelligence not elsewhere classified