Craig Boutilier
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: cebly@cs.ubc.ca
Abstract
Belief revision and belief update have been proposed as two
types of belief change serving different purposes,
revision intended to capture changes in belief state
reflecting new information about a static world, and update
intended to capture changes of belief in response to
a changing world. We argue that routine belief change involves
elements of both and present a model of generalized update
that allows updates in response to external changes to inform an
agent about its prior beliefs. This model of update combines aspects
of revision and update, providing a
more realistic characterization of belief change. We show that, under
certain assumptions, the original update postulates are satisfied.
We also demonstrate that plain revision and plain update are special
cases of our model. We also draw parallels to models of stochastic
dynamical systems, and use this to develop a model that
deals with iterated update and noisy observations in (qualitative
settings) that is analogous to Bayesian updating in a quantitative
setting.
To appear, Artificial Intelligence Journal
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