Uncertainty in Artificial Intelligence
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Generating New Beliefs From Old
Fahiem Bacchus, Adam Grove, Joseph Halpern, Daphne Koller
In previous work [BGHK92, BGHK93], we have studied the random-worlds approach -- a particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowledge base consisting of objective (first-order, statistical, and default) information. But allowing a knowledge base to contain only objective information is sometimes limiting. We occasionally wish to include information about degrees of belief in the knowledge base as well, because there are contexts in which old beliefs represent important information that should influence new beliefs. In this paper, we describe three quite general techniques for extending a method that generates degrees of belief from objective information to one that can make use of degrees of belief as well. All of our techniques are bloused on well-known approaches, such as cross-entropy. We discuss general connections between the techniques and in particular show that, although conceptually and technically quite different, all of the techniques give the same answer when applied to the random-worlds method.
Pages: 37-45
PS Link: http://www.cs.cornell.edu/home/halpern/papers/bghk_uai94.ps
PDF Link: /papers/94/p37-bacchus.pdf
AUTHOR = "Fahiem Bacchus and Adam Grove and Joseph Halpern and Daphne Koller",
TITLE = "Generating New Beliefs From Old",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "37--45"

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