Models for reasoning under uncertainty

Richard E. Neapolitan*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    4 Scopus citations

    Abstract

    As applied to expert systems, two models for reasoning under uncertainty (the well-known MYCIN model and a probability-based model) are described and compared. It is proven not only that the probabilistic assumptions for the probability-based model are weaker and therefore more intuitively appealing than those for MYCIN but also that, when two rules argue for the same conclusion, the combinatoric method in the probability-based model yields a higher combined certainty than that in the MYCIN model.

    Original languageEnglish (US)
    Pages (from-to)337-366
    Number of pages30
    JournalApplied Artificial Intelligence
    Volume1
    Issue number4
    DOIs
    StatePublished - Jan 1 1987

    ASJC Scopus subject areas

    • Artificial Intelligence

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