Uncertainty in Artificial Intelligence
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Rules, Belief Functions and Default Logic
Nic Wilson
Abstract:
This paper describes a natural framework for rules, based on belief functions, which includes a repre- sentation of numerical rules, default rules and rules allowing and rules not allowing contraposition. In particular it justifies the use of the Dempster-Shafer Theory for representing a particular class of rules, Belief calculated being a lower probability given certain independence assumptions on an underlying space. It shows how a belief function framework can be generalised to other logics, including a general Monte-Carlo algorithm for calculating belief, and how a version of Reiter's Default Logic can be seen as a limiting case of a belief function formalism.
Keywords:
Pages: 443-449
PS Link:
PDF Link: /papers/90/p443-wilson.pdf
BibTex:
@INPROCEEDINGS{Wilson90,
AUTHOR = "Nic Wilson ",
TITLE = "Rules, Belief Functions and Default Logic",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "443--449"
}


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