Craig Boutilier
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: cebly@cs.ubc.ca
Abstract
In this paper we describe two approaches to the revision of
probability functions. We assume that a probabilistic state of
belief is captured by a counterfactual probability or Popper
function, the revision of which determines a new Popper
function. We describe methods whereby the original function
determines the nature of the revised function.
The first is based on a probabilistic extension of Spohn's
OCFs, while the second exploits the structure implicit in
the Popper function itself. This stands in
contrast with previous
approaches that associate a unique Popper function with each
absolute (classical) probability function.
We also describe iterated revision using these models. Finally,
we consider the point of view that Popper functions may be
abstract representations of certain types of absolute
probability functions, but show that our revision methods
cannot be naturally interpreted as conditionalization on these functions.
(To appear, Notre Dame Journal of Formal Logic, 1995)
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