Been reading through the PLN book, largely because I really couldn't get hands-on on any examples or tutorials exploring various concepts of opencog in a more streamlined way. I am starting to get the impression that much of opencog is really just a framework for doing advanced machine learning work. Let me know how true that is.
Not really griping here either. PLN has been a good read. A good insight to have at this point is how PLN is *relevant* to the opencog framework. This might help me understand how PLN is used in a more practical context. The wiki page <http://wiki.opencog.org/w/Probabilistic_logic_networks> gives a little, it is far from what I'd call a satisfactory exposition. Where is the integration between concepts form PLN and Opencog itself ? Does PLN really just extend opencog, for that matter? Thanks for any feedback. -- 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 [email protected]. To post to this group, send email to [email protected]. 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/3fab4600-16aa-47e6-98ed-a7aab243eb0a%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
