Is Probability Theory Sufficient for Dealing with Uncertainty in AI: A Negative View
Lotfi Zadeh
Abstract:
An issue which has become a focus of controversy in recent years is whether or not classical probability theory is sufficient for dealing with uncertainty in Al. The topicality of this issue has grown as a result of the emergence of expert systems as one of the principal areas of activity in Al and the development of methods for evidential reasoning based on the DempsterSbafer theory and fuzzy logic which extend beyond the current boundaries of probability theory. A point of view which is articulated in this paper is that the inadequacy of probability theory stems from its lack of expressiveness as a language of uncertainty, especially for describing fuzzy events and fuzzy probabilities. For example, how would one represent the meaning of the proposition p: it is very likely that Mary is young, in which likely is a fuzzy probability and young is a fuzzy predicate? Furthermore, how can one infer from this proposition an answer to the question: What is the likelihood that Mary is not very young? We show through examples that problems of this type . problems which do not lend themselves to solution by conventional probabilitybased methods  can be dealt with effectively through the use of fuzzy logic.
Keywords: Classical Probability Theory, DempsterShafer
Pages: 103116
PS Link:
PDF Link: /papers/85/p103zadeh.pdf
BibTex:
@INPROCEEDINGS{Zadeh85,
AUTHOR = "Lotfi Zadeh
",
TITLE = "Is Probability Theory Sufficient for Dealing with Uncertainty in AI: A Negative View",
BOOKTITLE = "Uncertainty in Artificial Intelligence Annual Conference on Uncertainty in Artificial Intelligence (UAI85)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1985",
PAGES = "103116"
}

