YKY,

 

Here’s a great tutorial I found on how to go from logic, temporal in this case, 
to algebra using quantales J

http://www.qu.edu.qa/RelMiCs11/documents/tutAlgebraLogics.pdf

 

Great stuff here’s more by Höfner:

http://hoefner-online.de/home/html/publications.html

 

John

 

 

From: YKY (Yan King Yin, 甄景贤) [mailto:[email protected]] 



 

Thanks, I will have a look at the NCM thesis.

 

What I'm trying to do is similar to neural-symbolic integration, but my scope 
is broader, in the sense that I would consider any spatial technique, not just 
neural.

 

I have looked at a number of neural-symbolic proposals, but they don't seem to 
be particularly efficient.  So they proved that it is feasible, but they're 
still far from practical.

 

However, I am particularly impressed with the following:

 

1.  Paul Smolensky's "Tensor product variable binding and the representation of 
symbolic structures in connectionist systems" (1990).  (I think Ben recommended 
this one to me...)

It's capable of representing Lisp-like trees using neural networks, via vector 
sums and tensor products.  This is very close to my idea of using algebraic 
sums and products to represent logic formula trees.  I'm still trying to 
understand Smolensky's use of tensor products.

 

His book "The harmonic mind" (2006) may be easier to read.

 

2.  "Parsing Natural Scenes and Natural  Language with Recursive Neural 
Networks" Socher, Lin, Ng, Manning (2011) is also very impressive.  They're 
able to use a hybrid neural-tree structure to learn to parse natural language 
sentences and visual scenes.  Note: their ANN is "recursive" but not 
"recurrent", it's actually feed-forward.

 

It's very inspiring because parsing is a process that can require a logic 
engine, and yet they're able to use a neural network to perform the same 
function...  I'm trying to see where exactly the 'cheating' is taking place.... 
=)

 

Logic is slow;  my purpose is to replace the logic engine with something faster 
(but approximate), and yet not losing the universal expressive power of logic.

 


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