Hi,
> For instance, this means that the "cat" concept may well not be
> expressed by a single "cat" term, but perhaps by a complex learned
> (probabilistic) logical predicate.
I don't think it's really useful to discuss representing word meanings
without a sufficiently powerful notion of context (which is really hard).
Agreed. Most meanings in Novamente are context-relative, in fact...
> But my point for now is simply that all logic-based systems should not be
> damned based on the fact that historically a bunch of famous AI
> researchers have used logic-based KR in a cognitively unworkable way.
I certainly agree with that, as long as 'logic-based' means 'probabilistic
logic with bottom-up modelling and no unitary concepts or simple
word-symbol mappings'. Unfortunately many people would read 'logic
based' as 'looks like Cyc'.
Thanks -- you summarized one of my main points very effectively.
> Probabilistic logic is a general formalism that can express anything, and
> furthermore it can express any thing in a whole lot of different ways.
That isn't a point in its favour. Expressive scope allows people to say
'oh, our system could do that, it just needs the right
rules/network/whatever' whenever you ask them 'so how would your system
implement cognitive ability X?'. The limited expressive scope of classic
ANNs was actually essential for getting relatively naïve and simplistic
learning algorithms (e.g. backprop, Hebbian learning) to produce useful
solutions to an interesting (if still fairly narrow) class of problems.
Well, recurrent NN's also have universal applicability, just like probabilistic logic systems. And, this means that any general endorsement or condemnation of logic-based OR NN-based methods is pretty silly.... These are just very general tools, which may be used in many different ways.
Ben
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