Aaron <[email protected]> Hosford wrote:
I do think that reasoning and learning should always be running in parallel
to the behavioral and perceptual processes, and should be able to step in
and make adjustments when appropriate. That's the reason for going with a
universal format for all information processed by the system, namely
semantic nets.

I think that the data representation has to be simple because AGI is going
to be so complicated.  However, I totally disagree with the -conventional
notion of a semantic net-.  The idea of a semantic net is that of a network
based on a simplification of the categorization of relations of the
word-objects of the network using a few 'kinds' of abstractions to
characterize those relations.  Now you might say that the idea of a
semantic net could be improved on to make it capable of representing
potentially more profound insights, but my view is that it cannot be made
to fully accommodate the full extent of the meaning of words because if it
did it would not be what we typically think of when we think of a semantic
net.  I don't think you are *just* talking about a "universal format", but
of a heavy simplification process.

So whereas I do think that a simplifying process is necessary and I do
think that a universal format and something like a semantic net is a
good way to go, I am not talking about a traditional kind of semantic net
in which the relationship between words is found by a single abstraction
or by a handful of abstractions of the relations between words and
referential objects of the words and sentences.  This kind of semantic
net was based on a superficial analysis that indicated that the relations
between word-objects might be simplified using an concise list
of abstractions.  I am thinking of a relativistic semantic net where a
word-object can also become an abstraction of a relation or part of the
definition of a process of abstraction.

Jim Bromer



On Thu, Oct 18, 2012 at 10:27 AM, [email protected]
<[email protected]>wrote:

> Co-occurrence was really the wrong word. I forget it has the bag-of-words
> connotation. I imagine an efficient lookup could be designed by using a
> hash table with hash values based on a bag-of-words approach, but actual
> recognition would have to be based on the structure of the sentence, as you
> say.
>
> Anaphora resolution is designed into the system. The system doesn't pick a
> single object that can be matched by a pronoun. It picks a list of them
> based on recency of use, and links the pronoun to each of them via links
> with strength based on recency. It then performs higher-level analysis
> based on the object attributes indicated by the pronoun and the context in
> which the pronoun is used. Reasoning, which is as yet unimplemented, will
> be able to step in and further modify these link strengths based on
> additional information garnered from inference.
>
> This approach does produce some combinatorics, but with a reasonable upper
> bound dictated by the size of the recency list, which can be set to
> something comparable to the limits of human pronomial references and still
> be well within the computational constraints of the system.
>
> Interesting that you mention higher-level structure to the conversation
> being important to understanding. I recently read an article about a
> research team building a system that does exactly that, using a
> template-based approach. I am probably wildly wrong, but I *think* it was a
> fellow named Wilson and the system was named GENESYS. I'll look it back up
> and get you something definite here in a bit.
>
> I do think that reasoning and learning should always be running in
> parallel to the behavioral and perceptual processes, and should be able to
> step in and make adjustments when appropriate. That's the reason for going
> with a universal format for all information processed by the system, namely
> semantic nets.
>



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