John Musgrave

Notes on Uncertainty

Assertions are made by Pearl ‘88 - Probabilistic Reasoning in Intelligent Systems.

Encoding knowledge into rules requires enumerating examples. Positive examples are difficult to satisfy, and ambiguously defined. As a compromise, exceptions can be summarized. Each proposition can be assigned a measure of uncertainty which is aggregated. This uncertainty value is not a truth value, but closer to a counter-example. There is a restrictive assumption of independence. Three schools appear, non-monotonic logic which is non-numerical, probability calculus that is numerical including Demspter-Schaefer, fuzzy logic, and certainty factors, and probability theory, Bayesian probability.

(A^B) -> C
What do these propositions say about the interaction of A and B, and what are their exceptions?

Extensional systems use productions, and Intensional systems use declarative knowledge...

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