Belief revision and belief update have been proposed as two types of belief change serving differ ent purposes Belief revision is intended to capture changes of an agent's belief state reflecting new information about a static world Belief update is intended to capture changes of belief in response to a changing world. We argue that both belief revision and belief update are too restrictive, routine belief change involves elements of both. We present a model for generalized update that allows updates in response to external changes to inform the 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, in a way that formally verifies the intuition that revision is suitable for "static" belief change.
['Craig Boutiller']
Generalized update: belief change in dynamic settings