Belief Revision with Unreliable Observations

C. Boutilier, N. Friedman and J. Y. Halpern

To appear, Fifteenth National Conf. on Artificial Intelligence (AAAI), 1998.

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Research in belief revision has been dominated by work that lies firmly within the classic AGM paradigm, characterized by a well-known set of postulates governing the behavior of ``rational'' revision functions. A postulate that is rarely criticized is the success postulate: the result of revising by an observed proposition $\vphi$ results in belief in $\vphi$. This postulate, however, is often undesirable in settings where an agent's observations may be imprecise or noisy. We propose a semantics that captures a new ontology for studying revision functions, which can handle noisy observations in a natural way, while retaining the classical AGM model as a special case. We present a characterization theorem for our semantics, and describe a number of natural special cases that allow ease of specification and reasoning with revision functions. In particular, by making the Markov assumption, we can easily specify and reason about revision.

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