Uncertainty in Artificial Intelligence
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Non-Monotonicity in Probabilistic Reasoning
Benjamin Grosof
We start by defining an approach to non-monotonic probabilistic reasoning in terms of non-monotonic categorical (true-false) reasoning. We identify a type of non-monotonic probabilistic reasoning, akin to default inheritance, that is commonly found in practice, especially in "evidential" and "Bayesian" reasoning. We formulate this in terms of the Maximization of Conditional Independence (MCI), and identify a variety of applications for this sort of default. We propose a formalization using Pointwise Circumscription. We compare MCI to Maximum Entropy, another kind of non-monotonic principle, and conclude by raising a number of open questions
Keywords: Non-Monotonicity, Bayesian Reasoning, Maximum Entropy
Pages: 237-249
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PDF Link: /papers/86/p237-grosof.pdf
AUTHOR = "Benjamin Grosof ",
TITLE = "Non-Monotonicity in Probabilistic Reasoning",
BOOKTITLE = "Uncertainty in Artificial Intelligence 2 Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1986",
PAGES = "237--249"

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