Craig Boutilier
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: cebly@cs.ubc.ca
Nir Friedman
Computer Science Division
University of California
Berkeley, CA 94720, U.S.A.
email: nir@cs.berkeley.edu
Joseph Y. Halpern
Department of Computer Science
Cornell University
Ithaca, NY 14850, U.S.A.
email: halpern@cs.cornell.edu
Abstract
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 P results in
belief in P. 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.
Appeared AAAI, 1998
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