On 6/2/08, Ben Goertzel <[EMAIL PROTECTED]> wrote:
> eats(x, mouse)
That's a perfectly legitimate proposition. So it is perfectly OK to write:
> P( eats(x,mouse) )
Note here that I assume your "mouse" refers to a particular instance
of a mouse, as in:
eats(X, mouse_1234)
What's confusing is:
> for instance the term
>
> cat
>
> has probability
>
> P(cat)
>
> P(x is a cat | x is in my experience base)
In FOL, the term "term" means either a constant, a variable, or a
function applied to a tuple of other terms. In other words, terms in
FOL are "objects", not propositions.
Examples of terms in FOL:
stray_cat_1234
mary_queen_of_scots
X
mother(X)
mother(mary_queen_of_scots)
etc...
If you want to say
P ( X is a cat | X is in my experience base )
the corresponding FOL proposition should be:
cat(X)
instead of
cat.
I think your notation of "cat" translates to "cat(X)" in FOL.
Your experience base may contain an instances such as:
cat( stray_cat_1234 )
female( mary_queen_of_scots )
eats( cat_4567, mouse_890 )
etc...
> You can map terms and free-variable expressions into propositions if
> you want to, though...
It's a bit confusing to map OpenCog "terms" to FOL propositions. IMO
"terms" should not have probabilities attached to them. Anyway let me
just leave that decision to you. No more comments.
> However these propositional representations are a bit awkward and are
> not the way to represent things for the PLN rules to be simply
> applied... it is nicer by far to leave the experiential semantics
> implicit...
I'm interested to see how this is done.
1. The contents of your experience base can be translated to FOL.
2. Reasoning algorithms in FOL such as resolution are known to be
quite complex and slow.
3. You claim that your reasoning algorithm is faster.
4. That means, you've found a heuristic to reason quickly in FOL
(assuming your results can be translated back to FOL in polynomial
time).
More likely though, is that your algorithm is incomplete wrt FOL, ie,
there may be some things that FOL can infer but PLN can't. Either
that, or your algorithm may be actually slower than FOL.
YKY
-------------------------------------------
agi
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