Uncertainty in Artificial Intelligence
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Evaluation of Uncertain Inference Models I: PROSPECTOR
Robert Yadrick, Bruce Perrin, David Vaughan, Peter Holden, Karl Kempf
This paper examines the accuracy of the PROSPECTOR model for uncertain reasoning. PROSPECTOR's solutions for a large number of computer-generated inference networks were compared to those obtained from probability theory and minimum cross-entropy calculations. PROSPECTOR's answers were generally accurate for a restricted subset of problems that are consistent with its assumptions. However, even within this subset, we identified conditions under which PROSPECTOR's performance deteriorates.
Keywords: PROSPECTOR Model, Inference Networks, Probability Theory
Pages: 77-87
PS Link:
PDF Link: /papers/86/p77-yadrick.pdf
AUTHOR = "Robert Yadrick and Bruce Perrin and David Vaughan and Peter Holden and Karl Kempf",
TITLE = "Evaluation of Uncertain Inference Models I: PROSPECTOR",
BOOKTITLE = "Uncertainty in Artificial Intelligence 2 Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1986",
PAGES = "77--87"

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