Uncertainty in Artificial Intelligence
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Modifiable Combining Functions
Paul Cohen, Glenn Shafer, Prakash Shenoy
Modifiable combining functions are a synthesis of two common approaches to combining evidence. They offer many of the advantages of these approaches and avoid some disadvantages. Because they facilitate the acquisition, representation, explanation, and modification of knowledge about combinations of evidence, they are proposed as a tool for knowledge engineers who build systems that reason under uncertainty, not as a normative theory of evidence.
Keywords: Combining Functions, Combinations of Evidence
Pages: 10-21
PS Link:
PDF Link: /papers/87/p10-cohen.pdf
AUTHOR = "Paul Cohen and Glenn Shafer and Prakash Shenoy",
TITLE = "Modifiable Combining Functions",
BOOKTITLE = "Proceedings of the Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1987",
PAGES = "10--21"

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