Belief revision theory aims to describe how one should change one’s beliefs when they are contradicted by newly input information. The guiding principle of belief revision theory is to change one’s prior beliefs as little as possible in order to maintain consistency with the new information. Learning theory focuses, instead, on learning power: the ability to arrive at true beliefs in a wide range of possible environments. The goal of this paper is to bridge the two approaches by providing a learning theoretic analysis of the learning power of belief revision methods proposed by Spohn, Boutilier, Darwiche and Pearl, and others. The results indicate that learning power depends sharply on details of the methods. Hence, learning power can provide a well-motivated constraint on the design and implementation of concrete belief revision methods.
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Book Review: Neil Tennant, Changes of Mind: An Essay on Rational Belief Revision. [REVIEW]Nina Gierasimczuk - 2015 - Studia Logica 103 (1):227-231.
Maxi-Adjustment and Possibilistic Deduction for Adaptive Information Agents.Raymond Lau, Arthur H. M. ter Hofstede & Peter D. Bruza - 2001 - Journal of Applied Non-Classical Logics 11 (1-2):169-201.
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