Uncertainty in Artificial Intelligence
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Advantages and a Limitation of Using LEG Nets in a Real-TIme Problem
Thomas Slack
Abstract:
After experimenting with a number of non-probabilistic methods for dealing with uncertainty many researchers reaffirm a preference for probability methods [1] [2], although this remains controversial. The importance of being able to form decisions from incomplete data in diagnostic problems has highlighted probabilistic methods [5] which compute posterior probabilities from prior distributions in a way similar to Bayes Rule, and thus are called Bayesian methods. This paper documents the use of a Bayesian method in a real time problem which is similar to medical diagnosis in that there is a need to form decisions and take some action without complete knowledge of conditions in the problem domain. This particular method has a limitation which is discussed.
Keywords: Bayesian Method, Non-Probabilistic Methods
Pages: 421-428
PS Link:
PDF Link: /papers/87/p421-slack.pdf
BibTex:
@INPROCEEDINGS{Slack87,
AUTHOR = "Thomas Slack ",
TITLE = "Advantages and a Limitation of Using LEG Nets in a Real-TIme Problem",
BOOKTITLE = "Proceedings of the Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1987",
PAGES = "421--428"
}


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