Hi! There are two kinds of knowledge (or representations of knowledge) - symbolic (explicit, like logics, rules) and subsymbolic (black boxes like neural networks), And Your question is about possibility to transform the knowledge from the subsymbolic representation to the sybolic one. There are such efforts ineed. Quick Google search reveals article how to do logical reasoning (at least propositional and predication logic) with neural networks, how energy minimization of neural networks is similar to directed deduction process. So - symbolic knowledge can be encoded into subsymboli one. It can be trickier to go in other direction. I have no references about it but I guess it is possible - e.g. to extract logical rules from the trainned neural network classifier. There is branch of science called Connection Science that investigates the connection between neural networks and logics and this community have journal http://www.tandfonline.com/toc/ccos20/current
So - my understanding is that OpenCog can represent rules and other symbolic knowledge that are extracted from the subsymbolic representation of the knowledge, but it is not the only domain of application of OpenCog, there are lot more possible applications. -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to opencog+unsubscr...@googlegroups.com. To post to this group, send email to opencog@googlegroups.com. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/2d250c8f-71ad-4aec-85aa-584da1316e27%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
