Belief Revision with Unreliable Observations

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

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



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.