Uncertainty in Artificial Intelligence
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A Qualitative Markov Assumption and its Implications for Belief Change
Nir Friedman, Joseph Halpern
Abstract:
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 setting. 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 belief-change model.
Keywords: Belief change, Markov chains, qualitative probabilities.
Pages: 263-273
PS Link: http://www.cs.cornell.edu/home/halpern/papers/markov.ps
PDF Link: /papers/96/p263-friedman.pdf
BibTex:
@INPROCEEDINGS{Friedman96,
AUTHOR = "Nir Friedman and Joseph Halpern",
TITLE = "A Qualitative Markov Assumption and its Implications for Belief Change",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "263--273"
}


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