Hi,
It's easy enough to write out algebraic rules for manipulating fuzzy
qualifiers like "very likely", "may", and so forth. It may well be
that the human mind uses abstract, intuitive, algebraic-like rules for
manipulating these, instead of or in parallel to more quantitative
methods...
However, none of this is the subtle part of dealing with such
qualifiers. The subtle part is contextuality. For example the
English phrase "almost certainly" has different meaning in different
contexts --- and the word "many" has an even more wildly
context-variant significance.
The hard question is how does this context-specific adjustment of
meaning occur. NOT how to manipulate qualifiers within a context,
once they have already been contextually defined.
I think I know how to get this context-specific adjustment to work,
within the Novamente context of probabilistic and heuristic inference
using quantitative truth values. Pei has his own approach in the
context of his NARS system, also using quantitative truth values.
Loosemore believes (I think) this kind of contextual grounding comes
out of self-organizing neural-net-type dynamics that does not use any
notion analogous to numerical truth values -- even if it ultimately
results in something interpretable as a numerical truth value
assignable to a qualifier evaluated on an entity in a context.
So far as I can tell, your suggestion to use nonmonotonic logic does
not address this issue directly...
-- Ben G
On 8/4/06, Yan King Yin <[EMAIL PROTECTED]> wrote:
On 8/4/06, Pei Wang <[EMAIL PROTECTED]> wrote:
> The real alternative of numerical truth value is to stay with binary
> logic, which will make things even worse.
What I propose is to use symbolic, qualitative modifiers. For example:
1. this harddrive may be defective
2. this harddrive is very likely defective
3. this harddrive is almost certainly defective
4. if this harddrive is not defective, [insert joke]
so in principle I can create any number of nuances, if required. Most of
the time, facts would simply be in unmodified form. (Use nonmonotonic
reasoning to update them).
YKY ________________________________
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