Re: [agi] PLN and Bayes net comparison

2008-08-13 Thread YKY (Yan King Yin)
On 8/13/08, Ben Goertzel <[EMAIL PROTECTED]> wrote: > > But if one doesn't need to get into implementation details, in the simplest case one just has > > VariableScopeLink X > ImplicationLink > ___ANDLink > __ InheritanceLink X male > __ InheritanceLink X Unmarried > ___InheritanceLink

Re: [agi] PLN and Bayes net comparison

2008-08-13 Thread Ben Goertzel
But if one doesn't need to get into implementation details, in the simplest case one just has VariableScopeLink X ImplicationLink ___ANDLink __ InheritanceLink X male __ InheritanceLink X Unmarried ___InheritanceLink X bachelor where X is a Variable Node ... Then the Unification Rul

Re: [agi] PLN and Bayes net comparison

2008-08-13 Thread Ben Goertzel
YKY asked: > I'm interested in how the the rules are "fetched" from memory, and how the > variables get instantiated, etc... > > How would you represent the given facts: >"John is male" > "John is unmarried" > and then perform the inference to get > "John is a bachelor"? > > Sorry if

Re: [agi] PLN and Bayes net comparison

2008-08-12 Thread YKY (Yan King Yin)
On 8/12/08, Ben Goertzel <[EMAIL PROTECTED]> wrote: > construct 1 = > > ImplicationLink > ___ANDLink > __ PredicateNode isMale > __ PredicateNode isUnmarried > ___PredicateNode isBachelor > > It's just a relationship between functions (predicates being mathematical functions from entities

Re: [agi] PLN and Bayes net comparison

2008-08-12 Thread Ben Goertzel
Firstly, we can represent that using term logic without any explicit variables construct 1 = ImplicationLink ___ANDLink __ PredicateNode isMale __ PredicateNode isUnmarried ___PredicateNode isBachelor It's just a relationship between functions (predicates being mathematical functions fro

[agi] PLN and Bayes net comparison

2008-08-12 Thread YKY (Yan King Yin)
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 cont