On 8/13/08, rick the ponderer <[EMAIL PROTECTED]> wrote: > Thanks for replying YKY > Is the logic learning you are talking about inductive logic programming. If so, isn't ilp basically a search through the space of logic programs (i may be way off the mark here!), wouldn't it be too large of a search space to explore if you're trying reach agi. ************** Yes, and I guess the search space would be huge no matter what kind of learning substrate we use. At least one redeeming trick (for symbolic AI) is that we can limit the depth of the search of programs, and my intuition is that commonsense reasoning is mostly "shallow" (ie, involving few inference steps).
> And if you're determined to learn a symbolic representation, wouldn't genetic programming be a better choice, since it won't get stuck in local minima. ************* It is possible to use GA to search the ILP space; there is research in that area. I may use that too. One interesting question is to compare ILP search in the space of logic programs vs genetic programming (ie search in program spaces such as Lisp or combinator logic or lambda calculus). Unfortunately I'm unfamiliar with the latter, so I need some time to study that. > Would neural networks be better in that case because they have the mechanisms as in Geoff Hinton's paper that improve on random searching. ************** This is just the age-old debate of symbolic AI vs connectionism, given a new twist in the context of machine learning. Note that that first debate was never really settled. So, my bet is that we need NN-style learning at the low levels, and symbolic-style learning at the high levels. I tend to focus on the symbolic side. I'm very skeptical whether NN learning can solve high-level symbolic problems. > Also, if you did manage to learn a giant logic program that represented ai, could it be easily parallelized the way a neural network can be (so that it can run in real time). **************** Yes, logical inference can be parallelized. I have a book about it, but I haven't bothered to study that -- "design first, optimize later". YKY ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
