I'm in the process of reading this paper: http://www.jair.org/papers/paper1410.html
It might answer a couple of your questions. And, it looks like it has an interesting proposal about generating heuristics from the problem description. The setting is boolean rather than firs-order. It discusses the point about resolution being slow in practice. --Abram Demski On Tue, Sep 23, 2008 at 3:31 AM, YKY (Yan King Yin) <[EMAIL PROTECTED]> wrote: > On Thu, Sep 18, 2008 at 3:06 AM, Ben Goertzel <[EMAIL PROTECTED]> wrote: >> >> Prolog is not fast, it is painfully slow for complex inferences due to using >> backtracking as a control mechanism >> >> The time-complexity issue that matters for inference engines is >> inference-control ... i.e. dampening the combinatorial explosion (which >> backtracking does not do) >> >> Time-complexity issues within a single inference step can always be handled >> via mathematical or code optimization, whereas optimizing inference control >> is a deep, deep AI problem... >> >> So, actually, the main criterion for the AGI-friendliness of an inference >> scheme is whether it lends itself to flexible, adaptive control via >> >> -- taking long-term, cross-problem inference history into account >> >> -- learning appropriately from noninferential cognitive mechanisms (e.g. >> attention allocation...) > > (I've been busy implementing my AGI in Lisp recently...) > > I think optimization of single inference steps and using global > heuristics are both important. > > Prolog uses backtracking, but in my system I use all sorts of search > strategies, not to mention abduction and induction. Also, currently > I'm using general resolution instead of SLD resolution, which is for > Horn clauses only. But one problem I face is that when I want to deal > with equalities I have to use paramodulation (or some similar trick). > This makes things more complex and as you know, I don't like it! > > I wonder if PLN has a binary-logic subset, or is every TV > probabilistic by default? > > If you have a binary logic subset, then how does that subset differ > from classical logic? > > People have said many times that resolution is inefficient, but I have > never seen a theorem that says resolution is "slower" than other > deduction methods such as natural deduction or tableaux. All such > talk is based on anecdotal impressions. Also, I don't see why other > deduction methods are that much different from resolution since their > inference steps correspond to resolution steps very closely. Also, if > you can apply heuristics in other deduction methods you can do the > same with resolution. All in all, I see no reason why resolution is > inferior. > > So I'm wondering if there are some novel way of doing binary that > somehow makes inference faster than with classical logic. And exactly > what is the price to be paid? What aspects of classical logic are > lost? > > 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/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- 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=114414975-3c8e69 Powered by Listbox: http://www.listbox.com
