On 1/20/07, Pei Wang <[EMAIL PROTECTED]> wrote:
> The bottomline is that the knowledge acquisition project is *separable*
from
> specific inference methods.
What is your argument supporting this strong claim?
I guess every book on knowledge representation includes a statement
saying that whether a knowledge representation format is good, to a
large extent depends on the type of inference it can support. These
two aspects are never considered as separable.
Suppose I have a set of *deductive* facts/rules in FOPL. You can actually
use this data in your AGI to support other forms of inference such as
induction and abduction. In this sense the facts/rules collection does not
dictate the form of inference engine we use.
For example, First-Order Predicate Logic is not good enough for AI,
partly because it does not support non-deductive inference. Also,
semantic network is considered as "weak" mainly because it has no
powerful inference method associated.
FOPL can be used for things like induction and abduction, albeit via
external algorithms. Therefore, I think a FOPL-based system can suffice for
AGI (which doesn't mean that it is the only way).
I am still reading your book, but I found numerous good ideas in it. I know
that you treat deduction, induction, and abduction in a unified way. That
is a very elegant theory but it may have problems. For example, if:
(1) I read a lot of books
(2) I hate my mom
your system may infer by induction that "reading a lot of books -> hating
ones mom". In some instances doing this is meaningful, but in general your
system may be flooded with a lot of these speculative statements, drawing
time from the day-to-day deductive operations.
I tend to think of induction as something less essential than deduction.
That's why my top priority is to build an inference engine for deduction.
Inductive learning will be added later in the form of data mining, which is
very computation-intensive.
YKY
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