Uncertainty in Artificial Intelligence
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Belief Functions and Default Reasoning
Salem Benferhat, Alessandro Saffiotti, Philippe Smets
Abstract:
We present a new approach to dealing with default information based on the theory of belief functions. Our semantic structures, inspired by Adams' epsilon-semantics, are epsilon-belief assignments, where values committed to focal elements are either close to 0 or close to 1. We define two systems based on these structures, and relate them to other non-monotonic systems presented in the literature. We show that our second system correctly addresses the well-known problems of specificity, irrelevance, blocking of inheritance, ambiguity, and redundancy.
Keywords: Belief functions, default reasoning, non-monotonic reasoning, preferential systems,
Pages: 19-26
PS Link: ftp://iridia.ulb.ac.be/pub/saffiotti/uncertainty/uai_95.ps.gz
PDF Link: /papers/95/p19-benferhat.pdf
BibTex:
@INPROCEEDINGS{Benferhat95,
AUTHOR = "Salem Benferhat and Alessandro Saffiotti and Philippe Smets",
TITLE = "Belief Functions and Default Reasoning",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "19--26"
}


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