PLN can do inference on crisp-truth-valued statements ... and on this subset, it's equivalent to ordinary predicate logic ...
About resolution and inference: resolution is a single inference step. To make a theorem-prover, you must couple resolution with some search strategy. For a search strategy, Prolog uses backtracking, which is extremely crude. My beef is not with resolution but with backtracking. Another comment: even if one's premises and conclusion are crisp-truth-valued, it may still be worthwhile to deal with uncertain-truth-valued statements in the course of doing inference. Guesses, systematically managed, may help on the way from definite premises to definite conclusions... ben g 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 > -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC Director of Research, SIAI [EMAIL PROTECTED] "Nothing will ever be attempted if all possible objections must be first overcome " - Dr Samuel Johnson ------------------------------------------- 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
