On Feb 18, 2008 12:37 PM, Stephen Reed <[EMAIL PROTECTED]> wrote: > > Pei, > > Another issue with a KB inference engine as contrasted with a FOL theorem > prover is that the former seeks answers to queries, and the latter often > seeks to disprove the negation of the theorem by finding a contradiction. > Cycorp therefore could not reuse much of the research from the automatic > theorem proving community. And on the other hand the database community > commonly did not investigate deep inference.
The automatic theorem proving community does that because resolution by itself is not complete, while resolution-refutation is complete. Pei > As the Semantic Web community continues to develop new deductive inference > engines tuned to inference (ie. query answering) over large RDF KBs , I > expect to see open-source forward-chaining, and backward-chaining inference > engines that can be optimized in the same way that I described for Cyc. > > -Steve > > Stephen L. Reed > > Artificial Intelligence Researcher > http://texai.org/blog > http://texai.org > 3008 Oak Crest Ave. > Austin, Texas, USA 78704 > 512.791.7860 > > > > ----- Original Message ---- > From: Pei Wang <[EMAIL PROTECTED]> > To: [email protected] > > Sent: Monday, February 18, 2008 10:47:43 AM > Subject: Re: [agi] would anyone want to use a commonsense KB? > > Steve, > > I also agree with what you said, and what Cyc uses is no longer pure > resolution-based FOL. > > A purely resolution-based inference engine is mathematically elegant, > but completely impractical, because after all the knowledge are > transformed into the clause form required by resolution, most of the > semantic information in the knowledge structure is gone, and the > result is "equivalent" to the original knowledge in truth-value only. > It is hard to control the direction of the inference without semantic > information. > > Pei > > On Feb 18, 2008 11:13 AM, Stephen Reed <[EMAIL PROTECTED]> wrote: > > > > Pei: Resolution-based FOL on a huge KB is intractable. > > > > Agreed. > > > > However Cycorp spend a great deal of programming effort (i.e. many > > man-years) finding deep inference paths for common queries. The > strategies > > were: > > > > prune the rule set according to the context > > substitute procedural code for modus ponens in common query paths (e.g. > > isa-links inferred via graph traversal) > > structure the inference engine as a nested set of iterators so that easy > > answers are returned immediately, and harder-to-find answers trickle out > > later. > > establish a battery of inference engine controls (e.g. time bounds, speed > > vs. completeness - whether to employ expensive inference strategies for > > greater coverage of answers) and have the inference engine automatically > > apply the optimal control configuration for queries > > determine rule utility via machine learning and apply prioritized > inference > > modules within the given time constraints > > My last in-house talk at Cycorp, in the summer of 2006, described a notion > > of mine that Cyc's deductive inference engine behaves as an interpreter, > and > > that for a certain set of queries, a dramatic speed improvement (e.g. four > > orders of magnitude) could be achieved by compiling the query, and > possibly > > preprocessing incoming facts to suit expected queries. The queries that > > interested me were those embedded in an intelligent application, and which > > could be viewed as a query template with parameters. The compilation > > process I described would explore the parameter space with > programmer-chosen > > query examples. Then the resulting proof trees would be compiled into > > executable code - avoiding entirely the time consuming candidate rule > search > > and their application when the query executes. My notion for Cyc's > > deductive inference engine optimization is analogous to SQL query > > optimization technology. > > > > I expect to use this technique in the Texai project at the point when I > need > > a deductive inference engine. > > > > -Steve > > > > Stephen L. Reed > > > > Artificial Intelligence Researcher > > http://texai.org/blog > > http://texai.org > > 3008 Oak Crest Ave. > > Austin, Texas, USA 78704 > > 512.791.7860 > > > > > > > > ----- Original Message ---- > > From: Pei Wang <[EMAIL PROTECTED]> > > To: [email protected] > > Sent: Monday, February 18, 2008 6:17:59 AM > > Subject: Re: [agi] would anyone want to use a commonsense KB? > > > > On Feb 17, 2008 9:42 PM, YKY (Yan King Yin) > > > <[EMAIL PROTECTED]> wrote: > > > > > > So far I've been using resolution-based FOL, so there's only 1 inference > > > rule and this is not a big issue. If you're using nonstandard inference > > > rules, perhaps even approximate ones, I can see that this distinction is > > > important. > > > > Resolution-based FOL on a huge KB is intractable. > > > > Pei > > > > ------------------------------------------- > > agi > > Archives: http://www.listbox.com/member/archive/303/=now > > RSS Feed: http://www.listbox.com/member/archive/rss/303/ > > Modify Your Subscription: http://www.listbox.com/member/?&; > > > > > Powered by Listbox: http://www.listbox.com > > > > > > ________________________________ > > Be a better friend, newshound, and know-it-all with Yahoo! Mobile. 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