Perhaps the trick is to move the logic to some space where you can approximate Cn by a simple matrix multiplication, very much like a kernel method (perhaps you could apply the kernel trick for some form of Kernel Regression that gives the projection :-?).
Another (perhaps not so) related idea could be to apply some form of compressed sensing to the space of the arguments, which basically means that if the arguments involve a dense region of some (continuous) space, you may find a basis such that the projection is very sparse without loss of information. On Mon, Jan 20, 2014 at 9:15 AM, YKY (Yan King Yin, 甄景贤) < [email protected]> wrote: > > PS: A simplification is to break the consequence operator into "single > steps", which is a trick known in classical logic-based AI. So Cn(F) = Lim > St^k(F) as k -> infinity, where St is the single-step deduction operator. > > Even then, the St operator seems more complex than matrix multiplication, > as it involves matching the KB of facts with quantified logic formulas > (known as "rules"), via the unification algorithm. > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/15717384-a248fe41> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
