Hi,

The real "grounding problem" is the awkward and annoying fact that if
you presume a KR format, you can't reverse engineer a learning mechanism
that reliably fills that KR with knowledge.

Sure...

To go back to the source, in

http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad90.sgproblem.html

it is written

"
Suppose you had to learn Chinese as a first language and the only
source of information you had was a Chinese/Chinese dictionary![8]
This is more like the actual task faced by a purely symbolic model of
the mind: How can you ever get off the symbol/symbol merry-go-round?
How is symbol meaning to be grounded in something other than just more
meaningless symbols?[9] This is the symbol grounding problem
...
The standard reply of the symbolist (e.g., Fodor 1980, 1985) is that
the meaning of the symbols comes from connecting the symbol system to
the world "in the right way." But it seems apparent that the problem
of connecting up with the world in the right way is virtually
coextensive with the problem of cognition itself.
"

I suppose this is basically another way of saying the same thing that
you said above...

But, what I would say in response to you is: If you presume a **bad**
KR format, you can't match it with a learning mechanism that reliably
fills one's knowledge repository with knowledge...

If you presume a sufficiently and appropriately flexible KR format
(which is then really more of a meta-format), then it can reformat
itself adaptively based on the knowledge that comes in, as part of the
learning process ;-)

My conjecture is that a probabilistic weighted, labeled hypergraph --
with an appropriate collection of node/link types -- is a sufficiently
and appropriately flexible KR format, which can be made to adapt
itself based on the data within it, via coupling it with a careful
combination of evolutionary and inferential learning mechanisms...

-- Ben

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