Uncertainty in Artificial Intelligence
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Probabilistic Versus Fuzzy Reasoning
Peter Cheeseman
Abstract:
This paper shows how to solve problems using probability theory that the fuzzy approaches claim probability cannot solve. By using the view that probabilities are a measure of belief in a proposition in a particular context, limitations imposed by the frequency interpretations of probability are avoided. The various fuzzy approaches (fuzzy sets, fuzzy logic, possibility theory and higher order generalizations) seem to fill the gap caused by the restricted frequency interpretation. Close examination shows that the fuzzy approaches have exactly the same representation as the corresponding probabilistic approach and include similar calculi. The probabilistic approach assumes less information when the calculi differ.
Keywords: Probability Theory, Fuzzy Reasoning
Pages: 85-102
PS Link:
PDF Link: /papers/85/p85-cheeseman.pdf
BibTex:
@INPROCEEDINGS{Cheeseman85,
AUTHOR = "Peter Cheeseman ",
TITLE = "Probabilistic Versus Fuzzy Reasoning",
BOOKTITLE = "Uncertainty in Artificial Intelligence Annual Conference on Uncertainty in Artificial Intelligence (UAI-85)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1985",
PAGES = "85--102"
}


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