A Qualitative Markov Assumption and its Implications for Belief Change
N. Friedman and J. Y. Halpern
In Proc. Twelfth Conf. on
Uncertainty in Artificial Intelligence (UAI 96).
The study of belief change has been an active area in
philosophy and AI. In recent years two special cases of belief
change, belief revision and belief update, have
been studied in detail. Roughly, revision treats a surprising
observation as a sign that previous beliefs were wrong,
while update treats a surprising observation as an indication that
the world has changed.
In general, we would expect that an agent making an observation may
both want to revise some earlier beliefs and assume that some change has
occurred in the world.
We define a novel approach to belief change that allows us to do this, by
applying ideas from probability theory in a qualitative settings.
The key idea is to use a qualitative Markov assumption, which says
that state transitions are independent. We show that a recent
approach to modeling qualitative uncertainty using plausibility
measures allows us to make such a qualitative Markov assumption
in a relatively straightforward way, and show how the Markov assumption
can be used to provide an attractive
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