On 2/19/08, 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.
Thanks a lot for the info. These are very important speed-up strategies. I have not yet studied this aspect in detail. Can you explain what you mean by "deep inference"? I think resolution theorem proving provides a way to answer yes/no queries in a KB. I take it as a starting point, and try to think of ways to speed it up and to expand its abilities (answering what/where/when/who/how queries). YKY ------------------------------------------- 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/?member_id=8660244&id_secret=95818715-a78a9b Powered by Listbox: http://www.listbox.com
