Uncertainty in Artificial Intelligence
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Interval Structure: A Framework for Representing Uncertain Information
Michael Wong, L. Wang, Y. Yao
In this paper, a unified framework for representing uncertain information based on the notion of an interval structure is proposed. It is shown that the lower and upper approximations of the rough-set model, the lower and upper bounds of incidence calculus, and the belief and plausibility functions all obey the axioms of an interval structure. An interval structure can be used to synthesize the decision rules provided by the experts. An efficient algorithm to find the desirable set of rules is developed from a set of sound and complete inference axioms.
Pages: 336-343
PS Link:
PDF Link: /papers/92/p336-wong.pdf
AUTHOR = "Michael Wong and L. Wang and Y. Yao",
TITLE = "Interval Structure: A Framework for Representing Uncertain Information",
BOOKTITLE = "Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-92)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1992",
PAGES = "336--343"

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