> This is a draft of my paper.... =)
>
> I need some feedback before releasing it officially....
>
> Comments, suggestions, are welcome!
Hi YKY,
I found your paper interesting to read. You've got a nice story about
combining P and Z inference, and your integration of the two sounds quite
sensible.
Up to section 4 I was thinking "okay, this is all well and good, but how do
you do P & Z inference simultaneously", but then in section 5 when you
distribute P over Z and B, it answered my questions and it seemed like an
elegant solution.
However, I found myself worried about three things:
--------------------------
1. Why just P,Z and B?
Three mechanisms seems somewhat arbitrary - I think you need to make a very
compelling case for why there are three and only three mechanisms.
Or, more interestingly, I wonder if you could generalize the reasoning
framework so that every statement has a potentially unlimited number of
measures:
John is smart {fuzzy=0.7, probability=0.9, fuglepuaniness=0.4,
muargleness=0.5, braxillity=0.2, ...}
Santa moves extremely fast on Christmas night {fuzzy=1.0, probability=1.0,
realityness=0.1, ...}
(These extra measures might be interpretable concepts like
degree-of-context-dependency or truthfulness or degree-of-belief or
degree-of-fantasy, or they may be anonymous measures that were automatically
learnt/mined by machine learning algorithms)
You may have that some of these can be automatically converted to others
(like the way that B <-> Z), and you may have a hierarchy that says that
some measures distribute over other measures.
I wonder if, in such a framework, you could then present BPZ logic as a
specific instance that you have found, though practice and experimentation,
to strike a good balance between efficiency, learnability and accuracy. That
having probability distributions over probabilities fits into the framework,
but you found that it doesn't add enough value.
--------------------------
2. Why must the 12 combinations on page 19 be analysed separately?
Could these 12 cases be abstracted into some more general principle? Is
there some way of rethinking the reasoning mechanisms so that the 12 cases
don't seem to include arbitrary choices (e.g., "Convert the B variable to a
Z variable - but this is disallowed. We may convert the B variable
to a P(Z) variable and then invoke Case #8.").
--------------------------
3. How would you deal with context?
Something like "the water is hot" can mean different things depending on
whether you're talking about: making a cup of coffee, washing clothes, a
bathtub, a competitive swimming pool, or a glass of cool water without ice
on a hot day. Modifiers like "very" and "extremely" are similarly context
dependent, and gender, in particular, can have vastly different meanings
depending on context (even though we as humans are able to immediately
recognize what context is meant in most cases): social gender, genetic
gender, physical gender, gender identity, sexual gender, ...
--------------------------
A few other more superficial comments that might help improve the paper:
You present a "taxonomy of ignorance" (Figure 1) and assume it is
self-evident from the taxonomy that P and Z are sufficient. I certainly do
not find it to be self-evident: I don't see how the diagram supports your
argument.
Even though you apologize for not being politically correct, I still think
you should find better examples that do not have the potential to offend
readers. Rather than saying hermaphrodites are degree 0.6 typical human(?),
I'm sure you could find a simple and obvious example that addresses human
emotions that is less insensitive.
It doesn't look very professional when you cite Wikipedia.
-Ben
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agi
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