Hi Ben,
Hope you don't mind providing more clarification...
In first-order logic there may be a rule such as:
male(X) ^ unmarried(X) -> bachelor(X)
We can convert this to a probabilistic rule:
P(bachelor(X) = true | male(X) = true, unmarried(X) = true ) = 1.0
but note that this rule contains the variable X.
In my architecture, if I encounter a query such as:
"is John a bachelor?"
I would have to construct a propositional (ie, 0th-order) Bayes net to
answer that query. During the construction, I would instantiate the
rule with the variable substitution { X / John }.
In other words, in my architecture the KB is a collection of logical
formulae that do *not* form a network. Bayes nets are constructed
_on-the-fly_ to answer specific queries.
I'm not sure if PLN follows a similar arrangement. How can you have
everything represented as networks when some rules with variables can
be instantiated as many instances?
YKY
-------------------------------------------
agi
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