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Continuous Numeric Methods For Supporting Learning and Reasoning

Prof. John F. Sowa

VivoMind Intelligence, Inc. (USA)

Current systems for reasoning with large knowledge bases having ontologies of thousands of concept types and millions of facts and axioms have encountered serious difficulties: they are brittle, difficult to extend to new subjects, and take a long time for complex reasoning. Restricting the logic to a smaller, more tractable subset can often improve the speed, but it does not make the system less brittle or more extensible. Furthermore, it makes certain kinds of questions impossible to ask.

This talk presents some new computational methods that greatly improve the efficiency of searching for relevant information. They enable analogy finding, which takes N-cubed time with the older algorithms, to be performed in (N log N) time, where N is the number of assertions in the knowledge base. Analogies can be used to support less brittle, more extensible case-based reasoning, and they can also be used to align independently developed ontologies. The same methods that improve the speed of analogy finding can be used to improve other methods of reasoning, including induction, deduction, and abduction.

Wednesday, 13th July 2005, 5:15 p.m., University of Leipzig, Seminar Building, SG 03/63-64

Last updated: 23-Apr-2015 Time: 0.009 s